Production and Enrichment of Pancreatic Endocrine Progenitor Cells
20210340499 · 2021-11-04
Inventors
- Julie Beth Sneddon (San Francisco, CA, US)
- Lauren E. Byrnes (San Francisco, CA, US)
- Daniel M. Wong (San Francisco, CA, US)
Cpc classification
G01N33/74
PHYSICS
C12N5/0678
CHEMISTRY; METALLURGY
G01N2800/042
PHYSICS
International classification
Abstract
The disclosure provides methods for enriching for pancreatic endocrine progenitor cells, such as human pancreatic endocrine progenitor cells, including alpha cell progenitors, beta cell progenitors, delta cell progenitors, PP cell progenitors and epsilon cell progenitors. The disclosure provides mammalian, such as human, Fev.sup.+ pancreatic endocrine progenitor cells, including Fev.sup.+ alpha cell progenitors, Fev.sup.+ beta cell progenitors, Fev.sup.+ delta cell progenitors, Fev.sup.+ PP cell progenitors, and Fev.sup.+ epsilon cell progenitors. The disclosure further provides methods for producing or inducing such cells, including in vitro differentiation methods, and the cells so produced.
Claims
1. A method of enriching the pancreatic endocrine progenitor cell population in a cell sample comprising (a) detecting cells in the sample expressing a pancreatic endocrine progenitor cell marker; and (b) separating a pancreatic endocrine progenitor cell from at least one cell that does not express the pancreatic endocrine progenitor cell marker, thereby enriching the pancreatic endocrine progenitor cell population of the cell sample.
2. The method of claim 1, wherein the pancreatic endocrine progenitor cell is a human cell.
3. The method of claim 1, wherein the pancreatic endocrine progenitor cell is an alpha cell progenitor, a beta cell progenitor, a delta cell progenitor, a PP cell progenitor, or an epsilon cell progenitor.
4. The method of claim 3, wherein the pancreatic endocrine progenitor cell marker is the E26 transformation-specific transcription factor Fev.
5. The method of claim 4, wherein the pancreatic endocrine progenitor cell is a beta cell progenitor.
6. The method of claim 5, wherein the Fev.sup.+ beta cell progenitor further comprises Gng12.sup.+, Tssc4.sup.+, Ece1.sup.+, Tmcm108.sup.+, Wipi1.sup.+, or Papss2.sup.+.
7. The method of claim 6, wherein the beta cell progenitor is Fev.sup.+, Gng12.sup.+.
8. The method of claim 5, wherein the Fev.sup.+ beta cell progenitor further comprises Pax4.sup.+, Chga.sup.+, Chgb.sup.+, Neurod1.sup.+, Runx1t1.sup.+, or Vim.sup.+.
9. The method of claim 5 wherein the Fev.sup.+ beta cell progenitor does not express detectable Ngn3, Ins1 or Gcg.
10. The method of claim 9, wherein the beta cell progenitor is Fev.sup.+, Ngn.sup.−.
11. The method of claim 10, wherein the Fev.sup.+, Ngn.sup.− beta cell progenitor expresses a gene in the serotonin pathway, the insulin signaling pathway, sphingosine-1-phosphate signaling pathway, or Activating Transcription Factor-2.
12. The method of claim 5, wherein the Fev.sup.+ beta cell progenitor further comprises Pdx1.sup.+ or Mafb.sup.+.
13. The method of claim 1, wherein the at least one cell that does not express the pancreatic endocrine progenitor cell marker is a CD140.sup.+ mesenchyme cell.
14. The method of claim 5, wherein the beta cell progenitor cell is a human cell.
15. A method of producing a pancreatic endocrine progenitor cell comprising culturing a stem cell under conditions that induce differentiation of the stem cell into a pancreatic endocrine progenitor cell comprising the E26 transformation-specific transcription factor Fev.
16. The method of claim 15, wherein the stem cell is an embryonic stem cell (ESC) or an inducible pluripotent stem cell (iPSC).
17. The method of claim 15, wherein the pancreatic endocrine progenitor cell is an alpha cell progenitor, a beta cell progenitor, a delta cell progenitor, a PP cell progenitor, or an epsilon cell progenitor.
18. The method of claim 17, wherein the pancreatic endocrine progenitor cell is a beta cell progenitor.
19. The method of claim 18, wherein the Fev.sup.+ beta cell progenitor further comprises Gng12.sup.+, Tssc4.sup.+, Ece1.sup.+, Tmcm108.sup.+, Wipi1.sup.+, or Papss2.sup.+.
20. The method of claim 19, wherein the beta cell progenitor is Fev.sup.+, Gng12.sup.+.
21. The method of claim 18, wherein the Fev.sup.+ beta cell progenitor further comprises Pax4.sup.+, Chga.sup.+, Chgb.sup.+, Neurod1.sup.+, Runx1t1.sup.+, or Vim.sup.+.
22. The method of claim 18 wherein the Fev.sup.+ beta cell progenitor does not express detectable Ngn3, Ins1 or Gcg.
23. The method of claim 22, wherein the beta cell progenitor is Fev.sup.+, Ngn.sup.−.
24. The method of claim 23, wherein the Fev.sup.+, Ngn.sup.− beta cell progenitor expresses a gene in the serotonin pathway, the insulin signaling pathway, sphingosine-1-phosphate signaling pathway, or Activating Transcription Factor-2.
25. The method of claim 18, wherein the Fev.sup.+ beta cell progenitor further comprises Pdx1.sup.+ or Mafb.sup.+.
26. The method of claim 18, wherein the Fev.sup.+ beta cell progenitor is a human cell.
27. An isolated Fev.sup.+ pancreatic endocrine progenitor cell produced according to claim 17 or claim 18.
28. A method of inducing formation of a hormone-producing cell comprising contacting a progenitor of a hormone-producing cell with an effective amount of Fev to produce a hormone-producing cell.
29. The method of claim 28, wherein the hormone-producing cell is a INS+ cell.
30. The method of claim 29, wherein the hormone-producing progenitor cell is an ES4 cell.
31. The method of claim 28, wherein the hormone-producing cell is a beta cell.
32. The method of claim 31, wherein the hormone-producing progenitor cell is a beta-like cell.
33. The method of claim 28, wherein the method is performed in vitro.
34. The method of claim 28 further comprising removing a cell expressing at least one of PHOX2A, TLX2 or TBX2.
35. A method of screening for a signaling compound that induces FEV+ progenitor cell replication comprising: (a) contacting a FEV+ progenitor cell with a candidate compound; (b) culturing the FEV+ progenitor cell under conditions suitable for cell proliferation; (c) measuring the cell proliferation of the FEV+ progenitor cell in the presence or absence of the candidate compound; and (d) identifying the compound as a signaling compound for FEV+ progenitor cell proliferation if the cell proliferation in the presence of the compound is greater than the cell proliferation in the absence of the compound.
36. The method of claim 35 wherein the FEV+ progenitor cell is a FEV-MYC progenitor cell, a FEV-GFP progenitor cell, a FEV-KO progenitor cell, or a FEV-tNFGR progenitor cell.
37. A method of screening for a signaling compound that enhances FEV+ progenitor cell differentiation into beta cells comprising: (a) contacting FEV+ progenitor cells with a candidate compound; (b) incubating the FEV+ progenitor cells under conditions suitable for cell differentiation; (c) measuring the level of differentiation of the FEV+ progenitor cells to beta cells in the presence or absence of the candidate compound; and (d) identifying the compound as a signaling compound for FEV+ progenitor cell differentiation into beta cells if the cell differentiation in the presence of the compound is greater than the cell differentiation in the absence of the compound.
38. The method of claim 37 wherein the FEV+ progenitor cell is a FEV-MYC progenitor cell, a FEV-GFP progenitor cell, a FEV-KO progenitor cell, or a FEV-tNFGR progenitor cell.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
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DETAILED DESCRIPTION
[0051] Organogenesis requires the complex interactions of multiple cell lineages that coordinate their expansion, differentiation, and maturation over time. Utilizing a combination of single-cell RNA sequencing, immunofluorescence, in situ hybridization, and genetic lineage tracing, we profile the cell types within the epithelial and mesenchymal compartments of the murine pancreas across developmental time. We identify previously underappreciated cellular heterogeneity of the developing mesenchyme and reconstruct potential lineage relationships among the pancreatic mesothelium and novel mesenchymal cell types. Within the epithelium, we find a novel endocrine progenitor population, as well as an analogous population in both human fetal tissue and human embryonic stem cells differentiating towards a pancreatic beta cell fate. Further, we identify candidate transcriptional regulators along the differentiation trajectory of this population towards the alpha or beta cell lineages. This work establishes a roadmap of pancreatic development and demonstrates the broad utility of this approach for understanding lineage dynamics in developing organs.
[0052] The mesenchyme is critical for epithelial specification and proliferation throughout pancreatic development.sup.48-50, yet the individual cell types responsible for these processes remain unidentified. Our single-cell dataset has enabled the identification of multiple novel mesenchymal populations, highlighted the transcriptional dynamism of the pancreatic mesothelium, and predicted lineage relationships among the mesothelium and VSM populations. Secreted factors, such as mesothelial-derived Fgf9, may play a similar role in the pancreas as in the lung, where it regulates mesenchymal cell proliferation and vascular formation.sup.51. While previous studies identified Cxcl12 (highly expressed in our dataset in cluster 4) as a regulator of pancreatic epithelial specification, differentiation, and adult regeneration.sup.52,53, these studies focused on the epithelium and did not define a role for mesenchymally-derived Cxci12. Finally, secretion of Wnt antagonists by cluster 5 may regulate processes regulated by Wnt signaling in the developing pancreas, including epithelial specification, expansion, and exocrine development.sup.54. Future work can focus on uncovering the functions of these individual mesenchymal populations in development, physiology, and pathology of the pancreas.
[0053] With the various cell types of the mesenchyme now enumerated and their markers identified, we can begin to elucidate the maturation and lineage relationships of the pancreatic mesenchymal compartment. Our time course data have provided evidence of maturation within the mesothelial population. Genes such as Pitx2, kallikren 13 (Kik13) and 8 (Kik8), were differentially expressed in younger, E12.5, mesothelial cells. Pitx2 regulates differentiation in multiple systems.sup.27,57-60, and the kallikren family are serine proteases that are involved in extracellular matrix and adhesive molecule degradation.sup.55. Expression of these genes leads to the expectation that the E12.5 mesothelial population is primed for migration and differentiation. In contrast, the E17.5 mesothelial population expressed genes related to barrier or immune function, such as dermokine (Dmkn).sup.56,57, bone marrow stromal antigen 2 (Bst2), and retinoic acid receptor responder 2 (Rarres2).sup.58. These results establish stage-dependent roles for the mesothelium throughout development.
[0054] The different roles for the mesothelium across time are also evident from our pseudotime analysis, which predicts that the mesothelium serves as a progenitor of other mesenchymal cell types during development. Indeed, the mesothelium is a critical mesenchymal progenitor population in other organs, such as the heart, intestine, lung, and liver.sup.14-17. The data disclosed herein indicate that mesothelial progenitor activity occurs at E12.5 or earlier during pancreatic development, consistent with other organ systems.sup.11,14,16. Indeed, a recent study identified that parietal mesothelial cells can function as progenitor cells prior to pancreatic specification.sup.59. In vivo lineage tracing studies will verify the predictions from these pseudotime analyses, and the transcriptomic information obtained by this study will allow the development of tools to target individual populations within the mesenchyme and perform lineage tracing, ablation, and expression studies.
[0055] The study of the mesothelium in development is also relevant for fibrotic diseases of adult organs, as factors secreted by mesothelial cells and mesothelial-derived, disease-driving myofibroblasts modulate organ responses to injury.sup.60-62. Fibrotic diseases of the adult pancreas are characterized by aberrant recapitulation of developmental pathways within the epithelium.sup.63,64. We can now utilize our developmental dataset to probe the mesenchymal populations during adult homeostasis and disease states, and compare to the populations detected throughout development. Therefore, this dataset serves as a broad resource for the implementation of future studies in pancreatic mesenchymal biology.
[0056] Within the epithelial compartment, our identification of a novel Fev.sup.Hi endocrine progenitor population provides increased resolution of endocrine differentiation. The relative timing of expression of canonical endocrine lineage genes can now be mapped onto these additional differentiation stages. Several lines of evidence identify the gene Fev as a direct target of Ngn3: Fev is the transcription factor most strongly expressed in Ngn3.sup.+ endocrine progenitors.sup.65, and Ngn3 knockout embryos do not exhibit Fev expression in the developing pancreas.sup.24. Known target genes of Ngn3, such as Pax4.sup.66 and Runx1t1i.sup.67, are expressed by the early-stage Fev.sup.+/Pax4.sup.+ population. Additionally, Pax6 was upregulated within the Fev.sup.Hi population. Although Chga and Chgb are often utilized as markers of differentiated endocrine lineages, we found that Chga and Chgb are expressed in the Fev.sup.Hi population prior to hormone acquisition. This result is consistent with previous work that identified Chga.sup.+, hormone.sup.− cells in rodent pancreatic development.sup.68. The Fev.sup.Hi cell stage likely represents the cell stage during endocrine differentiation preceding specialized hormone production and may now serve as a cellular landmark for understanding endocrine lineage gene expression dynamics.
[0057] The gene Fev has been previously studied mainly in serotonergic neurons, where it is a master transcription factor required for cellular differentiation and maturation, as well as serotonin synthesis.sup.28. Fev switches transcriptional targets from differentiation genes during development to maturation genes postnatally in serotonergic neurons.sup.69. In an insulinoma cell line, Fev directly binds to the regulatory regions of serotonergic genes, such as Tph1, Tph2, Ddc, Slc18a2, and Slc6a4, as well as the Ins1 promoter itself.sup.24. Future ChIP-seq studies of embryonic pancreas will globally identify direct targets of Fev and Fev-regulated transcriptional networks in developing endocrine cells.
[0058] Using genetic lineage tracing in vivo, we have demonstrated that all five endocrine lineages of the developing pancreas transit through a Fev-expressing stage, and that Fev.sup.− lineage cells contribute not only to embryonic, but also to adult pancreatic endocrine cells. The fraction of epsilon cells that are not derived from a Fev.sup.− lineage may represent the subset of ghrelin.sup.+ cells previously reported to give rise to cells of the ductal and exocrine lineages 30. Given that all adult gamma cells are Fev.sup.− lineage labeled, the small subset of gamma cells that are not lineage traced during pancreatic development may represent those that do not persist in the adult pancreas. Further highlighting the relevance of Fev.sup.Hi progenitors during pancreatic development, our pseudotime analysis revealed that Fev-expressing cells may be pre-specified towards an alpha or beta cell fate (
[0059] For the eventual application of this knowledge to human therapeutics, it is important to validate that the predicted relationships hold true in the context of human pancreatic development. Our staining of human fetal pancreas identified the analogous FEV.sup.Hi population, consistent with our findings in murine pancreata. Directed differentiation of hESCs towards endocrine cell fates will provide a platform for modeling and manipulating the predicted lineage regulators found in this study. Indeed, we have identified a FEV.sup.+ population within hESC-derived endocrine progenitor cells. Deeper knowledge of these lineage decisions may substantially improve directed differentiation efforts to efficiently generate functional beta cells for cellular replacement therapy for patients with diabetes. This study highlights the power of combining single-cell transcriptomic information with in vivo lineage tracing to reconstruct developmental trajectories within cellular compartments. Identification of novel populations and their lineage relationships will promote discovery of the mechanisms that drive lineage decisions and commitment.
[0060] The following examples are presented by way of illustration and are not intended to limit the scope of the subject matter disclosed herein.
EXAMPLES
Example 1
[0061] Materials and Methods
[0062] Mice
[0063] All mouse procedures were approved by the University of California, San Francisco (UCSF) Institutional Animal Care and Use Committee (IACUC). Mice were housed in a 12-hour light-dark cycle in a controlled temperature climate. Noon of the day of vaginal plug was considered embryonic day 0.5.
[0064] Timed-pregnant Swiss Webster mice were obtained from Charles River Laboratories. Ngn3-Cre.sup.70, Fev-Cre.sup.71, ROSA26mTmG 31 mice have been previously described and were maintained in a C57BL/6J background.
[0065] Human Tissue Procurement and Isolation
[0066] Human fetal pancreata were harvested from post-mortem fetuses at 23 weeks of gestation with permission from the ethical committee of the University of California, San Francisco (UCSF). Tissue was fixed in 4% paraformaldehyde overnight at 4° C. After three washes in 1×PBS, tissue was either cryopreserved in 30% sucrose solution at 4° C. overnight and embedded in OCT, or placed in 40% ethanol then 70% ethanol before paraffin embedding. 8 um sections were cut on the cryostat or microtome. In situ hybridization and immunofluorescence were then performed as described below.
[0067] Adult human islets were isolated from cadaveric donor tissue by the UCSF Islet Production Core with permission from the UCSF ethical committee. Consented cadaver donor pancreata were provided by the nationally recognized organization UNOS via local organ procurement agencies. The identifiers were maintained at the source only, and the investigators received de-identified specimens.
[0068] Informed consent was obtained for all human (fetal and adult) tissue collection, and protocols were approved by the Human Research Protection Program Committee on Human Research of the University of California, San Francisco (UCSF).
[0069] Embryonic Stem Cell Culture and Differentiation to the Endocrine Lineage
[0070] The human embryonic stem cell (hESC) line HUES8 was obtained from Harvard University and used for the generation of hESC-derived β-like cells (BLCs). Pluripotent HUES8 cells were maintained as spherical clusters in suspension in mTeSR-1 (StemCell Technologies) in 500 mL spinner flasks (Corning, VWR) on a magnetic stir plate (Dura-Mag) within a 37° C. incubator with 5% CO2, 100% humidity, and a rotation rate of 70 rpm. Cells were screened for mycoplasma contamination using the MycoProbe Mycoplasma Detection Kit (R&D Systems), according to the manufacturer's instructions.
[0071] hESC-derived endocrine progenitor cells were generated as previously described.sup.32. In brief, HUES8 cells were seeded into a spinner flask at a concentration of 8×10.sup.5 cells/mL in mTeSR-1 media with 101.iM Rock inhibitor Y27632 to allow formation of spherical clusters. Differentiation was initiated 72 hours later. Differentiation was achieved in a step-wise fashion using the following growth factors and/or small molecules: definitive endoderm cells (Stage 1) (Activin A 100 ng/mL, R&D Systems; CHIR99021 141.ig/mL, Stemgent); gut tube endoderm cells (Stage 2) (KGF 50 ng/mL, Peprotech); early pancreatic progenitors (Stage 3) (LDN193189 200 nM, Fisher Scientific; KGF 50 ng/mL, Peprotech; Sant-1 0.251.iM, Sigma; Retinoic Acid 21.iM, Sigma; PdbU 500 nM, EMD Biosciences); later pancreatic progenitors (Stage 4) (KGF 50 ng/mL, Peprotech; Sant-1 0.251.iM, Sigma; Retinoic Acid 0.11.iM, Sigma); endocrine progenitors (Stage 5) (Sant-1 0.251.iM, Sigma; Retinoic Acid 0.11.iM, Sigma; XXI 11.iM, EMD Millipore; Alk5i 101.iM, Axxora; T3 11.iM, EMD Biosciences; Betacellulin 20 ng/mL, Fisher Scientific), BLCs (Stage 6) (Alk5i; T3). Successful differentiation was assessed at the definitive endoderm, pancreatic progenitor 1, pancreatic progenitor 2, and endocrine progenitor stages via immunofluorescence or FACS for stage-specific marker genes.
[0072] To measure the expression of FEV at various stages of human endocrine differentiation, aliquots of clusters were removed from the flask and analyzed at several time points: after 5 days in Stage 5 (“mid-stage endocrine progenitors”), after 7 days in Stage 5 (“late-stage endocrine progenitors”), and after 5 days at the BLC stage. As a comparator, pluripotent, undifferentiated hESCs in mTeSR-1, as well as human adult islets, were also analyzed for FEV expression.
[0073] Immunofluorescence
[0074] Embryonic mouse pancreata were dissected in cold 1×PBS and fixed in zinc-buffered formalin (Anatech LTD) at room temperature (RT) for 30-90 minutes or overnight at 4° C. After three washes in 1×PBS, tissue was processed for either cryopreservation or paraffin embedding. Cryopreserved pancreata were placed in 30% sucrose solution at 4° C. overnight before embedding in OCT. Paraffin-embedded pancreata were placed in 40% ethanol and 70% ethanol before paraffin tissue processing. 8 um sections were cut on the cryostat or microtome. For immunofluorescence on paraffin sections, slides were baked at 55° C. for 30 minutes, deparaffinized in xylene, and rehydrated in decreasing concentrations of ethanol. Heat-mediated antigen retrieval was performed using Antigen Retrieval Citra Solution (Biogenex Laboratories). Tissue sections were blocked in 5% normal donkey serum (NDS; Rockland Immunochemicals) and Mouse-on-Mouse IgG blocking reagent (Vector Laboratories) when appropriate in 0.2% Triton X-100 in PBS (PBT) for 1 hour and then stained overnight at 4° C. using the following primary antibodies: Acta2 (1:200, Abcam ab21027), Cav1 (1:200, Abcam ab2910), Chromogranin A (1:100, Abcam ab15160), E-cadherin (1:200, BD Transduction Lab 610182), Glucagon (1:100, Abcam ab82270), Insulin (1:50, DAKO A0564), Vimentin (1:200, Abcam ab92547), and Wt1 (1:100, Abcam ab89901). All antibodies have been validated by the manufacturer. The next day, sections were washed three times in 0.1% Tween 20 in 1×PBS and then incubated with species-specific Alexa Fluor 488-, 594-, or 647-conjugated secondary antibodies (1:500, Jackson ImmunoResearch) and DAPI in 5% NDS in 0.2% PBT for 1 hour at RT. Sections were washed three times in 0.1% Tween 20 in 1×PBS, rinsed in 1×PBS, and then mounted in Fluoromount-G mounting medium (Southern Biotech). Slides were stored at 4° C.
[0075] For immunofluorescence on cryosections, slides were removed from −80° C. storage and allowed to reach RT. Sections were rinsed in 1×PBS three times and permeabilized in 0.5% PBT for 10 minutes at RT. Tissue sections were blocked in 5% NDS and, if needed, Mouse-on-Mouse IgG blocking reagent in 0.1% PBT for 1 hour and then stained overnight at 4° C. using the following primary antibodies: Epcam (1:200, BD Transduction Lab 552370), Glucagon (1:2000, Millipore 4031-01F), Insulin (1:250, DAKO A0564), Somatostatin (1:500, Santa Cruz Biotechnology sc-7819, Ghrelin (1:1500, Santa Cruz Biotechnology sc-10368), Pancreatic Polypeptide (PPY; 1:250, Abcam ab77192), and Vimentin (1:200, Abcam ab92547). All antibodies have been validated by manufacturer. Sections were washed the next day three times in 1×PBS and then incubated with species-specific Alexa Fluor 488-, 555-, 594-, or 647-conjugated secondary antibodies and DAPI in 5% NDS in 0.1% PBT for 1 hour at RT. Sections were washed three times in 1×PBS and mounted in Fluoromount-G mounting medium. Slides were stored at 4° C.
[0076] Images were captured on a Zeiss Apotome Widefield microscope with optical sectioning capabilities or Leica confocal laser scanning SP8 microscope. Maximum intensity z-projections were then prepared using ImageJ, where brightness, contrast, and pseudo-coloring adjustments were applied equally across all images in a given series.
[0077] In Situ Hybridization
[0078] In situ hybridization was performed on 8 um sections as previously describee using RNAscope technology (Advanced Cell Diagnostics).sup.73 according to the manufacturer's instructions. In situ probes against mouse Ngn3 (422409-C2), Fev (413241-C3), Isl1 (451931), Ins1 (414661-C4), Gcg (400601), Sst (404631-C3), Ghrl (415301-C2), Ppy (482701), Peg10 (512921-C4), Gng12 (462521-C2), Nnat (432631-C2), Barx1 (414681), Pitx2 (412841-C2), Stmn2 (498391-C3), Msln (443241) and human NGN3 (505791-C4), FEV (471421-C3), and ISL1 (478591-C2) were used in combination with the RNAscope Multiplex Fluorescent Reagent Kit v2 for target detection. Following signal amplification of the target probes, sections were washed in 1×PBS three times and blocked in 5% NDS in 0.1% PBT for 1 hour at RT. Tissue sections were then stained with primary and secondary antibodies as described above in the “immunofluorescence” section.
[0079] For in situ hybridization of hESC-derived clusters, cells were fixed with 4% PFA for 15 minutes at RT, washed with PBS, and cryoprotected in 30% sucrose overnight. The next day, clusters were embedded in a small sphere of 1.5% low-melting temperature agarose; these were again cryoprotected in 30% sucrose overnight. The following day, the agarose spheres were soaked in OCT and frozen in a dry ice bath. In situ hybridization was then performed on 8 um sections using human NGN3, FEV, and ISL1 RNAscope probes.
[0080] Quantification of Cell Proportions
[0081] Quantification of pancreata was performed by manual counting using ImageJ software. Cell populations present at less than 1% in Ngn3-lineage-traced E14.5 replicates were deemed artifact and excluded from further analysis.
[0082] Quantitative RT-PCR
[0083] hESCs from various stages of directed differentiation were collected and RNA extracted with the RNeasy Mini Kit (Qiagen). Reverse transcription was performed with the Clontech RT-PCR kit. RT-PCR was run on a 7900HT Fast Real-Time PCR instrument (Applied Biosystems) with Taqman probes for FEV (assay ID: Hs00232733_m1) and GAPDH (assay ID: Hs02758991_g1) in triplicate. Data were normalized to GAPDH. Error bars represent standard deviation.
[0084] Dissociation and FACS of Embryonic Pancreas
[0085] Embryonic mouse pancreata were dissected and placed in 1×PBS on ice, then dissociated into single cells using TrypLE Express dissociation reagent (Thermo Fisher) at 37° C. with pipet trituration at 5-minute intervals during incubation. For v1 datasets, E12.5 pancreata were dissociated for 10 minutes, E14.5 pancreata for 15 minutes, and E17.5 pancreata for 30 minutes. For batch 1, we pooled 14 E14.5 pancreata from one litter. For batch 2, which was collected on a different day, we pooled tissue from each time point separately: 18 E12.5 pancreata from two litters, 11 E14.5 pancreata from one litter, and 8 E17.5 pancreata from one litter. Dissociations were neutralized with FACS buffer (10% FBS+2 mM EDTA in phenol-red free HBSS). Dissociated cells were passed through a 30 um cell strainer and stained with Sytox live/dead stain (Thermo Fisher). Stained cells were washed twice in FACS buffer and then sorted using a BD FACS Aria II. After size selection to remove doublets, all live cells were collected.
[0086] For version 2 10× datasets, we pooled tissue from each time point separately, each performed on a different day: 14 E12.5 pancreata from one litter, 13 E14.5 pancreata from one litter, and 13 E17.5 pancreata from one litter. For the E14.5 Fev-Cre; ROSA26.sup.mTmG 10× sample, we pooled 3 pancreata from one litter. Dissociations were performed as described above. Cells undergoing a CD140a negative selection were stained with CD140a-APC (1:50; eBiosciences, cat. 17-1401-81; validated by manufacturer). Stained cells were washed twice in FACS buffer and then sorted using a BD FACS Aria II. After size selection to remove doublets, all live CD140a.sup.− cells were collected. For the E14.5 Fev-Cre; mTmG pancreata, live GFP.sup.+ cells and GFP.sup.−/TdTomato.sup.+ cells were collected. All 4,000 GFP.sup.+ (Fev-lineage-traced) cells were loaded onto the 10× Genomics platform, supplemented with an additional 21,000 TdTomato.sup.+/GFP.sup.− (non-lineage-traced).
[0087] Single-Cell Capture and Sequencing
[0088] To capture individual cells, we utilized the Chromium Single Cell 3′ Reagent Version 1 Kit (10× Genomics).sup.74. For batch 1, 12,800 cells from E14.5 pancreata were loaded into one well of the 10× chip, while for batch 2, 18,000 cells per time point were each loaded into their own respective wells to produce Gel Bead-in-Emulsions (GEMs). GEMs underwent reverse transcription to barcode RNA before cleanup and cDNA amplification. Libraries were prepared with the Chromium Single Cell 3′ Reagent Version 1 Kit. Each sample was sequenced on 2 (Batch 1) or 1 (Batch 2) lanes of the HiSeq2500 (Illumina) in Rapid Run Mode with paired-end sequencing parameters: Read1, 98 cycles; Index1, 14 cycles; Index2, 8 cycles; and Read2, 10 cycles.
[0089] The CD140a-depleted E12.5, E14.5, and E17.5 datasets and Fev-Cre; ROSA26mTmG dataset in
[0090] Single-Cell Analysis
[0091] For the v1 datasets, we utilized CellRanger v1.1.0 software for v1 datasets and v2.1.0 for v2 datasets with default settings for de-multiplexing, aligning reads to the mouse genome (10× Genomics pre-build mm10 reference genome) with STAR′ and counting unique molecular identifiers (UMIs) to build transcriptomic profiles of individual cells. For the v1 datasets, gene barcode matrices were analyzed with the R package Seurat v1.4, using the online tutorial as a guide.sup.7,76. We first performed a filtering step, retaining only the cells that expressed a minimum of 200 genes and only the genes that were expressed in at least 3 cells. A large number of cells did not meet this threshold in the E17.5 time point and were determined to be red blood cells by the high expression of hemoglobin genes. Variable genes were determined by mean-variance relationship to identify highly expressed and variable genes with the Seurat function MeanVarPlot with default settings. UMI counts were log-normalized, and linear regression was performed with RegressOut to account for differences in the number of UMIs between cells. Principal component analysis (PCA) was then utilized to determine sources of variability in the dataset with PCAfast. Significant PCs were determined based on the Scree plot and utilized for Seurat's graph-based clustering algorithm (function FindClusters) with default parameters, except for the resolution parameter. To vary cluster numbers, the resolution parameter in FindClusters was adjusted from 0.6-3.0, and resulting clusters analyzed as follows. Clusters were visualized with t-distributed stochastic neighbor embedding (t-SNE) with Seurat's RunTSNE function with default settings.sup.77. Differentially expressed genes were determined with the FindAllMarkers function, which uses a bimodal likelihood ratio test.sup.8. We confirmed differential gene expression analysis with the Wilcox rank sum test and MAST.sup.9 utilizing Seurat v2's FindMarkers function with default settings. These tests calculate adjusted p-values for multiple comparisons. To determine the final number of clusters, clusters were required to have at least 9 significantly (p<0.05) differentially expressed genes with a 2-fold difference in expression in comparison to all other clusters. Clusters were manually curated for differential gene expression, and those that did not meet this threshold were manually merged with the nearest cluster based on the phylogenetic tree from Seurat's BuildClusterTree. In some cases, clusters met the 9-gene threshold but appeared to have very similar differentially expressed genes to another cluster. This is likely a result of the comparison of individual clusters against all other clusters in determining differentially expressed genes. In these cases, a pairwise comparison between the two clusters was performed and the same 9-gene threshold applied. An exception to the 9-gene threshold was made to annotate the proliferating population in early stages of the cell cycle within the E14.5 mesenchymal analysis (
[0092] Custom Genome Build
[0093] The custom genome for alignment of reads to eGFP and TdTomato sequences from the mTmG mouse line was created according to instructions provided by 10× Genomics reference support (https://support.10×genomics.com/single-cell-gene-expression/software/pipelines/latest/advanced/references). eGFP and TdTomato sequences were concatenated to the mm10-2.1.0 reference genome (FASTA file) provided by 10× Genomics. eGFP and TdTomato annotations were then concatenated to the mm10 annotations (GTF file) provided by 10× Genomics. The Cellranger mkref command was then utilized with the genome and annotations with eGFP and TdTomato, as described in the above link.
[0094] Pathway Analysis
[0095] Pathway analysis and calculation of associated p-values were performed using the ConsensusPathDB over-representation analysis for pathway-based sets category (http://cpdb.molgen.mpg.de).sup.78.
[0096] Aggregating E17.5 v2 Datasets
[0097] E17.5 technical replicates from the v2 dataset were aggregated with Cellranger v2.1, utilizing the aggr function with default settings. The aggregated dataset was used for analysis and merging with the E12.5 and E14.5 v2 datasets.
[0098] Sub-Clustering and Merging Datasets
[0099] Sub-clustering was performed by isolating clusters of interest with the Seurat function SubsetData and reanalyzing as described above. Cells were classified as epithelial based on the expression of E-cadherin (Cdh1) and other known epithelial population markers. Cells that were Cdh1.sup.−; Vim.sup.+, and collagen3a1 (Col3a1).sup.+ were classified as mesenchymal. Multiple batches were merged with the MergeSeurat function. The merged dataset was reanalyzed as above, with batch included as a latent variable in the RegressOut function. The v1 E14.5 batch 1 and batch 2 clusters were robust to the sampling differences between batches as evidenced by the contribution of cells from both batches to each cluster (
[0100] For v2 datasets (E12.5, E14.5 and E17.5), multiple canonical correlation analysis (multiCCA) from Seurat v2.3 was utilized to merge the epithelial datasets.sup.36. The top 1,000 most highly variable genes that were variable in at least 2 datasets were used for the alignment, as recommended in the Seurat tutorial. The shared correlation strength of each CC was measured with Seurat's MetageneBicorPlot, and those before the drop-off were used for alignment, analogous to the Scree plot in choosing significant PCs. We then aligned the datasets with AlignSubspace and ran an integrated t-SNE and clustering analysis, as outlined in the Seurat tutorial. Clusters were required to have 9 significantly differentially expressed genes as described above. Clusters with similar differentially expressed genes were verified with pairwise comparisons to the most related clusters (based on BuildClusterTree) and merged if they did not meet the pairwise 9-gene threshold. The Beta 2 cluster in the v2 endocrine merged time course data met the 9-gene threshold for 2 out of the 3 differential expression tests (Bimodal likelihood ratio and Wilcox rank sum tests), but had only 8 differentially expressed genes for the MAST test. Doublets were identified based on co-expression of two mutually exclusive genes, such as both mesenchymal and epithelial genes, and removed from further analysis. In the v2 datasets, rare cells (4 cells in E12.5 and 13 cells in E14.5 endocrine datasets) with high levels of hemoglobin gene expression were removed from the analysis.
[0101] Downsampling Analysis
[0102] To determine if the sequencing depth was sufficient for calling clusters, downsampling analysis was performed for the v1 E14.5 batch 1 dataset. Reads were randomly downsampled from the 10× Cellranger bam file output to a specified percentage, then grouped based on UMI to generate a count profile for each cell. The number of genes with greater than 0 counts was then calculated. UMI downsampling was performed with the SampleUMI function. A new Seurat object was created with the downsampled matrix and reanalyzed as above.
[0103] The number of UMIs/cell was downsampled from an average of 4,600 UMIs/cell in the full dataset to 200 UMIs/cell, and the median number of genes/cell and clustering robustness was then calculated. Clustering robustness was determined as the percentage of cells within the same cluster, with clusters required to maintain at least 9 genes with a 2-fold change in expression in comparison to all other clusters. Within this dataset, robust clustering was maintained all the way down to 500 UMIs/cell, when the percentage of cells in the same cluster began to climb, indicating collapsing of individual clusters. Both of these downsampling analyses indicate that sufficient sequencing depth was reached.
[0104] Pseudotemporal Ordering
[0105] We utilized Monocle 2.6.4.sup.79 to order cells in “pseudotime” based on their transcriptomic similarity. For v1 time course datasets, batch-corrected values and variable genes from Seurat analysis were used as input, utilizing the gaussianff expressionFamily, and clusters were projected onto the minimum spanning tree after ordering.
[0106] For the Fev-lineage-traced dataset, UMI counts and variable genes from the Seurat analysis were used as input, utilizing the negBinom expressionFamily. To find genes differentially expressed across the branch point in the trajectory, we used monocle's internal BEAM analysis and selected genes with an FDR cutoff of 0.001. Gene expression patterns were plotted with plot_genes_branched_heatmap and plot_multiple_branches_pseudotime.
[0107] Code Availability
[0108] Scripts are available at https://github.com/sneddonucsf/2018-Developmental-single-cell-RNA-sequencing.
[0109] Data Availability
[0110] The accession number for the raw data files of the single-cell RNA sequencing analyses disclosed herein is GEO: GSE101099. The sequence data is incorporated herein by reference.
Example 2
[0111] Cellular Heterogeneity in the Murine Pancreas
[0112] We first set out to characterize the major sources of cellular heterogeneity in the developing pancreas, in the most unbiased fashion possible. Two batches of mouse pancreata at E14.5, a particularly active time of expansion, morphogenesis, and diversification.sup.6 (
Example 3
[0113] Identification of Novel Mesenchymal Populations
[0114] While previous studies have identified numerous markers of the various pancreatic epithelial populations.sup.6, comparatively little is known about heterogeneity among pancreatic mesenchymal cells or how they change over developmental time. We therefore turned our attention to the mesenchymal compartment by sub-clustering only mesenchymal cells (5,069 cells) and re-performing the clustering analysis (
[0115] The remaining mesenchymal clusters included proliferating mesenchymal cells (clusters 6, 7, and 8), a large cluster (cluster 10) that expressed pan-mesenchymal markers, and four clusters (clusters 2, 4, 5, and 9) each expressing a distinct signature that segregated them from cluster 10 (
Example 4
[0116] Mesothelial Cells Undergo Transcriptional Changes Across Developmental Time
[0117] During organogenesis, the dynamics of each lineage are defined by the expansion, differentiation, and maturation of its constituent cells. To begin addressing how these processes change across chronological time within the developing pancreas, we performed single-cell sequencing of pancreas at two additional time points, E12.5 and E17.5 (
[0118] While the mesothelium is a well-established mesenchymal progenitor cell population for VSM and fibroblasts in multiple other organs, both the role of the mesothelium and the origin of the mesenchymal cell types within the pancreas remain uncharacterized.sup.14-17. We utilized our single-cell mesenchymal dataset to determine whether the pancreatic mesothelium may function as a mesenchymal progenitor cell population during development. We found six populations (clusters 2, 3, 4, 5, 12, and 13) that expressed VSM cell genes, such as Acta2 and Tagin, or genes known to regulate VSM development, such as Mgp.sup.18, Fhl1.sup.19,20, Barx1.sup.21, and Pitx2.sup.22 (
[0119] To test the lineage relationships among these populations, we ordered cells in pseudotime based on their transcriptional similarity.sup.23. This analysis placed mesothelial cells on one side of the pseudotime trajectory (
Example 5
[0120] Identification of a Novel Endocrine Progenitor Population
[0121] After assessing the heterogeneity within the mesenchymal compartment, we next focused on the epithelial cells. We first sub-clustered the 2,049 cells from our E14.5 dataset that comprised just the epithelial populations (
[0122] After the ductal, acinar, Ngn3.sup.+, and hormone.sup.+ populations had been accounted for, there still remained one population that eluded classification based on known marker genes. This novel population could be distinguished from all other epithelial populations by high-level expression of the E26 transformation-specific (ETS) transcription factor Fev, previously shown to be expressed within the developing pancreas but not described as a marker of a distinct population of epithelial cells.sup.24 (
[0123] Further sub-clustering of all cells within the endocrine lineage (661 cells) revealed additional sub-groups of Fev-expressing cells. The first was marked by high expression of Pax4 and Runx1 Translocation Partner 1 (Runx1t1) and lower levels of Ngn3. The second was marked by Chgb and Vimentin (Vim) (
[0124] Given that the novel Fev.sup.+ populations expressed endocrine lineage genes, we utilized pseudotime ordering.sup.23 to test the expectation that both Fev.sup.+ populations were lineage-related to the Ngn3+ progenitors that give rise to the endocrine compartment of the pancreas.sup.29. This de novo reconstruction of the developmental trajectory placed both the Fev.sup.+/Pax4.sup.+ and Fev.sup.Hi/Chgb.sup.+ cells between Ngn3.sup.+ endocrine progenitors and alpha and beta cells (
[0125] To validate these predicted lineage relationships, we performed an in vivo lineage trace of Ngn3.sup.+ cells. In E14.5 Ngn3-Cre; ROSA26.sup.mTmG mouse pancreata, where lineage-traced cells are membrane-GFP.sup.+31, approximately 20% of all Ngn3-lineage-traced cells were identified as the predicted Fev.sup.HI population by the presence of Fev and the absence of both Ngn3 and the pan.sup.− differentiated endocrine cell marker Islet1 (Isl1) (
[0126] We next tested if the Fev.sup.Hi population was also present in developing human pancreatic tissue. In human fetal pancreas at 23 weeks of gestation, we observed cells that only expressed NGN3 (
[0127] We then probed hESCs undergoing directed differentiation towards the pancreatic beta cell lineage in vitro.sup.32. FEV transcript was detected in endocrine progenitor-stage cells and beta-like cells (BLCs) at levels comparable to adult human islets, but it was not detected in undifferentiated hESCs (
Example 6
[0128] Endocrine Dynamics Over Developmental Time
[0129] Although we had captured comparatively fewer epithelial cells at E12.5 and E17.5 than at E14.5, we could still identify the Fev.sup.Hi cells at both time points (
[0130] To analyze how endocrine populations change overtime, we merged all three v2 time points into one dataset using canonical correlation analysis.sup.36. We correlated the v2 dataset to the v1 dataset and could identify all populations present in the v1 dataset (
Example 7
[0131] Lineage Decisions within the Endocrine Compartment
[0132] As the in vivo lineage tracing data had revealed that the Fev.sup.Hi population is derived from the Ngn3.sup.+ population, we expected that the Fev.sup.Hi population could then function as a progenitor for the endocrine populations of the developing pancreas. We utilized a Fev-Cre; ROSA26.sup.mTmG lineage tracing strategy to label Fev-expressing cells and their progeny. We found 100% of alpha, 100% of beta, 100% of delta, 89.1% of gamma, and 23.8% of epsilon cells were Fev-lineage-traced in E14.5 pancreas (
[0133] With evidence in vivo that the majority of endocrine cells pass through a Fev-expressing stage, we next combined this lineage tracing approach with single-cell RNA sequencing to identify transcriptional regulators of endocrine differentiation. We used Fev-Cre; ROSA26mTmG pancreata to enrich for Fev-expressing cells and their progeny (membrane-GFP.sup.+) at E14.5 with FACS sorting (
[0134] We next set out to predict the lineage relationships among the endocrine cells and identify transcriptional regulators of differentiation. Pseudotime ordering identified a trajectory that began with Ngn3.sup.+ cells, transitioned into Fev.sup.+ cells, and then split into two main branches (
[0135] We next used an analysis tool in the Monocle software called branched expression analysis modeling (BEAM) to identify the genes that distinguish the paths along the two branches to either alpha or beta cells. We found gene clusters that were upregulated along different segments of the pseudotime trajectory (
[0136] To validate the predictions of our pseudotime analysis, we performed in situ hybridization for markers that defined each branch of the trajectory. First, we confirmed the expression of Peg10 and Gng12 within the Fev.sup.Hi population (
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Example 8
[0217] Materials and Methods
[0218] The materials and methods for experiments involving human cells and tissues, as disclosed in Examples 9-18 and elsewhere in the application, are presented in this Example.
[0219] Human Cells and Tissues
[0220] Expanding on the work described in preceding Examples identifying a novel pancreatic endocrine population in mice marked by the expression of the gene Fev, we have successfully verified the existence of an analogous FEV-expressing endocrine progenitor population in human cells as well. In particular, we have identified the transcriptional profile of this FEV-expressing pancreatic endocrine progenitor population in human cells. Using genomic engineering techniques (utilizing CRISPR-Cas9 editing), a number of relevant human embryonic stem cell (hESC) lines were created, including FEV-Myc, which is an hESC line in which the FEV gene has been tagged with a protein (Myc), facilitating application of ChIP-Seq technology to identify the regions of the genome to which FEV binds in pancreatic progenitor cells. A second developed cell line is FEV-GFP, which is an hESC line in which FEV expression is reported by the presence of a green fluorescent protein. The FEV-GFP line is important for isolating FEV-expressing cells from the heterogeneous culture of hESC-derived cells as they are being directed in their differentiation towards a pancreatic beta cell fate, for instance. A third cell line developed is the FEV-KO line, which is an hESC line in which the FEV gene has been deleted (knocked out). We have now performed experiments with the FEV-KO line and shown that when the gene FEV is ablated, hESC cells suffer a significant reduction in the number of pancreatic beta cells that can be made. This provides evidence that the gene itself is functionally important in this pancreatic endocrine population.
[0221] More particularly, the data disclosed herein reveals an unknown endocrine progenitor stage that is defined by high expression of Fev, a transcription factor. The data shows that all hormone-expressing endocrine lineages of the murine pancreas transit through a Fev-expressing cell stage. The data disclosed herein further establishes that similar FEV-expressing endocrine progenitor cell populations are found in human pancreatic development.
[0222] Given that FEV+ progenitors constitute a major stage in human endocrine cell differentiation, novel tools have been developed to study both the function of FEV and FEV-expressing cells in in vitro beta cell differentiation. This work forms a foundation on which improvements to in vitro beta cell differentiation can be made to more closely reflect proper endocrine cell development in vivo, thereby increasing beta cell yield at the end of this process and generating beta cells that are functional in vitro and in vivo.
[0223] The experimental data disclosed herein establish that the novel endocrine progenitor stage defined by differential expression of the transcription factor named Fev are Fev+ endocrine progenitors derived from Ngn3+ progenitors. The Fev+ endocrine progenitors give rise to hormone-expressing lineages of the murine pancreas.
[0224] This map of in vitro beta cell differentiation uncovers a novel lineage that results from mis-differentiation of FEV-expressing progenitors and opens avenues through which current in vitro beta cell differentiation methods can be improved for greater differentiation efficiency. Given our findings of Fev/FEV+ progenitors in murine development, human fetal development, and the in vitro derivation of beta cells, valuable hESC lines were engineered to study the function of the FEV gene and FEV+ endocrine progenitors during the directed differentiation of beta cells. It is expected tat FEV+ endocrine progenitor cells can also be directed to differentiate into other hormone-expressing endocrine lineages. This work resulted in a new differentiation model for human endocrine cell development and paves the way for improved in vitro pancreatic progenitor cell derivation methods that better reflect in vivo human endocrine cell development and can lead to a variety of endocrine cell types, including beta cells, alpha cells, delta cells and the like.
[0225] Human Tissue Procurement, Isolation, and Processing
[0226] Human fetal pancreata were harvested from post-mortem fetuses with approval from the ethical committee of UCSF. Tissue was obtained through two sources: the University of Washington Birth Defects Research Laboratory (12wpc_1 and 15.5wpc samples) and Advanced Bioscience Resources, Inc (12wpc_2 and 16wpc samples). Tissue was harvested at respective clinics and shipped overnight on ice in either 1×PBS (samples from University of Washington Birth Defects Research Laboratory) or RPMI media (samples from Advanced Bioscience Resources, Inc). Following delivery, tissue was washed once with 1×PBS, minced with a sterile scalpel, and dissociated in Liberase™ and 0.1 mg/mL DNase in 1×HBSS for 30-55 minutes in a 37° C. Thermomixer programmed to shake at 1000 rpm. Dissociation was quenched with 5 mM EDTA and 10% FBS in 1×HBSS. Cell suspension was filtered through a 30 μm cell strainer. Red blood cells (RBCs) were removed from the cell suspension using immunomagnetic negative selection with STEMCELL Technologies' EasySep RBC Depletion Reagent (cat. no. 18170). Following RBC depletion, cells were counted and loaded onto the 10× Chromium Platform for single-cell RNA-sequencing.
[0227] Adult human islets were isolated from cadaveric donor tissue by the UCSF Islet Production Core with approval from the UCSF ethical committee. Consented cadaver donor pancreata were provided by the nationally recognized organization UNOS via local organ procurement agencies. The identifiers were maintained at the source only, and the investigators received de-identified specimens.
[0228] Informed consent was obtained for all human (fetal and adult) tissue collection, and protocols were approved by the Human Research protection Program Committee on Human Research of UCSF.
[0229] Embryonic Stem Cell Culture and Differentiation
[0230] The hESC line HUES8 was obtained from Harvard University and used for the generation of hESC-derived beta-like cells (BLCs). Pluripotent HUES8 cells were maintained as spherical clusters in suspension in mTeSR-1 (StemCell Technologies) in 500 mL spinner flasks (Corning, VWR) on a magnetic stir plate (Dura-Mag) within a 37° C. incubator at 5% CO2, 100% humidity, and a rotation rate of 70 rpm. Cells were screened for mycoplasma contamination using the MycoProbe Mycoplasma Detection Kit (R&D Systems), according to the manufacturer's instructions.
[0231] BLCs were generated as previously described (Pagliuca et al., 2014), with additional modifications (Millman et al., 2016). In brief, HUES8 cells were seeded into a spinner flask at a concentration of 8×10.sup.5 cells/mL in mTeSR1 media with 10 μM Rock inhibitor Y-27632 (STEMCELL Technologies) to allow formation of spherical clusters. Differentiation was initiated 72 hours later. Differentiation was achieved in a step-wise fashion using the following growth factors and/or small molecules: definitive endoderm (Stage 1) (1 day of 100 ng/mL Activin A (R&D Systems) and 14 μg/mL of CHIR99021 (Stemgent); 2 days of 100 ng/mL Activin A); gut tube endoderm (Stage 2) (3 days of 50 ng/mL KGF (Peprotech)); early pancreatic progenitors (Stage 3) (1 day of 200 nM LDN193189 (Fisher Scientific), 50 ng/mL KGF, 0.25 μM SANT-1 (Sigma), 2 μM Retinoic Acid (Sigma), 500 nM PdbU (EMD Biosciences), and 10 μM Rock inhibitor Y-27632 (STEMCELL Technologies); 1 day of 50 ng/mL KGF, 0.25 μM SANT-1, 2 μM Retinoic Acid, 500 nM PdbU); later pancreatic progenitors (Stage 4) (5 days of 50 ng/mL KGF, 0.25 μM SANT-1, 0.1 μM Retinoic Acid, and 10 μM Rock inhibitor Y-27632); endocrine progenitors (Stage 5) (4 days of 0.25 μM SANT-1, 0.1 μM Retinoic Acid, 1 μM XXI (EMD Millipore), 10 μM Alk5i (Axxora), 1 μM T3 (EMD Biosciences), 20 ng/mL Betacellulin (Fisher Scientific); 3 days of 25 nM Retinoic Acid, 1 μM XXI, 10 μM Alk5i, 1 μM T3, 20 ng/mL Betacellulin); BLCs (Stage 6) (6-11 days of 10 μM Alk5i; 1 μM T3). Successful differentiation was assessed at the completion of Stages 1, 3, 4, 5, and 6 via immunofluorescence or FACS for stage-specific marker genes. hESC-derived cells used for single-cell RNA-sequencing were taken at ES4 (End of Stage 4), S5D4 (Stage 5, Day 4), S5D7, S6D4, and S6D10. Cells for single-cell RNA-sequencing were dissociated with Accumax for 15-25 minutes in a 37° C. water bath. The dissociated cell suspension was neutralized with stage-specific media and filtered through a 37 μm filter. Cells were counted and then loaded onto the 10× Chromium Platform for single-cell RNA-sequencing.
[0232] In Situ Hybridization and Immunofluorescence of hESC-Derived Clusters
[0233] hESC-derived cell clusters were fixed in 4% PFA in 1×PBS for 15 minutes at room temperature (RT). Fixed clusters were washed with 1×PBS and cryoprotected overnight at 4° C. in 30% sucrose. Clusters were then embedded in OCT, and 8 μm sections were cut.
[0234] In situ hybridization was performed on 8 μm sections using RNAscope technology (Advanced Cell Diagnostics) according to the manufacturer's instructions. An in situ probe against human FEV (cat. no. 471421-C3) was used in combination with the RNAscope Multiplex Fluorescent Reagent Kit v2 for target detection. Following signal amplification of the target probes, sections were washed in 1×PBS three times and blocked in 5% normal donkey serum (NDS, Rockland Immunochemicals) in 0.1% Triton X-100 in PBS for 1 hour at RT. Tissue sections were then stained with a primary antibody against PDX1 (1:100, R&D Systems). The next day, sections were washed three times in 0.1% Tween 20 in 1×PBS and then incubated with species-specific Alexa Fluor 488-secondary antibodies (1:500, Jackson ImmunoResearch) and DAPI in 5% NDS in 0.2% PBT for 1 hour at RT. Sections were washed three times in 0.1% Tween 20 in 1×PBS, rinsed in 1×PBS, and then mounted in ProLong Gold Mounting Medium. Slides were stored at 4° C.
[0235] Images were captured on a Leica confocal laser scanning SP8 microscope. Maximum intensity z-projections were then prepared using ImageJ, where brightness, contrast, and pseudo-coloring adjustments were applied equally across all images in a given series.
[0236] Quantitative RT-PCR
[0237] hESC-derived cells at various stages of directed differentiation were collected in Trizol, and RNA was extracted with the Direct-zol RNA Miniprep kit (Zymo Research). Adult human islets were also processed this same manner for RNA extraction. Reverse transcription was performed with the Superscript IV First-Strand Synthesis System (Thermo Fisher Scientific, cat. no. 18091050) using Oligo d(T) primers and random hexamers. RT-PCR was run on an ABI Real-Time PCR System (Applied Biosystems, 384-well format) with Taqman probes for FEV (assay ID: Hs00232733_m1) and GAPDH (assay ID: Hs02758991_g1) in triplicate. Data were normalized to GAPDH.
[0238] Single-Cell Capture and Sequencing
[0239] To capture individual cells, we utilized the Chromium Single Cell 3′ Reagent Version 3 Kit (10× Genomics) (Zheng et al., 2017). Only the 15.5wpc sample was processed with the Chromium Single Cell 3′ Reagent Version 2 Kit. For all samples, 25,000 cells were loaded onto one or two wells of the 10× chip to produce Gel Bead-in-Emulsions (GEMs). GEMs underwent reverse transcription to barcode RNA before cleanup and cDNA amplification. Libraries were prepared with the Chromium Single Cell 3′ Reagent Kit. Each sample was sequenced on the NovaSeq (Illumina) in Rapid Run Mode with paired-end sequencing parameters: Read1, 98 cycles; Index1, 14 cycles; Index2, 8 cycles; and Read2, 10 cycles.
[0240] Single-Cell Analysis
[0241] CellRanger v3.0.2 software was used for all single-cell RNA-sequencing datasets with default settings for de-multiplexing, aligning reads to the human genome (10× Genomics pre-built hg38 reference genome) with STAR (Dobin et al., 2012) and counting unique molecular identifiers (UMIs) to build transcriptomic profiles of individual cells. Gene-barcode matrices were analyzed with the R package Seurat v3.0.1 (Stuart et al., 2019). We first performed a filtering step, retaining only the cells that expressed a minimum and maximum number of genes and did not exceed a specified percentage of reads that map to the mitochondrial genome. The following quality control metrics for each dataset are outlined in Table 6.
TABLE-US-00001 TABLE 6 Quality control metrics for human single-cell sequencing analyses. Minimum Maximum Maximum Number Number Percentage Of Sample Name Of Genes Of Genes Mitochondrial Gene 12wpc_1 200 4,000 15 12wpc_2 200 5,000 15 15.5wpc 200 4,000 7.5 16wpc 200 6,000 15 ES4 200 6,000 10 S5D4 200 6,000 12.5 S5D7 200 6,000 15 S6D4 200 6,000 15 S6D10 200 6,000 15
Sample name is listed along with the minimum and maximum number of genes and maximum percentage of mitochondrial genes used for quality control thresholds.
[0242] Data were then normalized with the Seurat3 function NormalizeData with default settings. This employs a global-scaling normalization that normalizes gene expression measurements for each cell by the total expression. Genes that exhibit high cell-to-cell variation were then identified using FindVariableFeatures. The highly variable genes from this analysis were then used in downstream analysis to highlight biological signal from background noise in single-cell datasets. Data then underwent linear transformation (“scaling”), which was required prior to dimensional reduction with PCA, and this scaling was done with ScaleData. PCA (Principal Component Analysis) was performed on the scaled data with RunPCA. Significant PCs (principal components) were determined with ElbowPlot, which plots principal components based on the percentage of variance exhibited by each one. These significant PCs were utilized in Seurat3's graph-based clustering algorithms, FindNeighbors and FindClusters. The resolution parameter of FindClusters was adjusted to vary the number of clusters found by the algorithm. Clusters were visualized by UMAP with Seurat3's RunUMAP and DimPlot functions. Differentially expressed genes were determined with the FindAllMarkers function. Seurat3's VInPlot, DotPlot, and FeaturePlot functions were used to visualize of expression of genes of interest across cells and clusters.
[0243] Sub-Clustering and Merging Datasets
[0244] Sub-clustering was performed by isolating clusters of interest with the Seurat3 function Subset and reanalyzing as outlined above (finding variable genes, scaling data, and identification of significant PCs). Cells were classified as endocrine based on the expression of Chromogranin A (CHGA).
[0245] Merging of all human fetal datasets was accomplished with Seurat3's Integration workflow. This integration workflow in Seurat3 identifies “anchors” across disparate single-cell datasets in order to construct harmonized references for better merging of the data and minimization of batch effect (Stuart et al., 2019). In the integration workflow, all datasets were merged into a single Seurat object and processed to the step encompassing identification of variable genes (FindVariableFeatures). Integration anchors were then identified using the FindIntegrationAnchors and used to integrate all human fetal datasets through the IntegrateData function. Following integration, data were scaled (ScaleData), significant PCs were identified (RunPCA), and UMAP-based clustering was performed (RunUMAP, FindNeighbors, FindClusters). Gene expression of specific genes were visualized by using read levels from the “RNA” slot of the integrated Seurat object (accessed by inputting “rna_gene” into gene parameter).
[0246] Pseudotemporal Ordering
[0247] For the pseudotemporal ordering analysis of the 12wpc_1 sample, we utilized Monocle v2.99.3 (named Monocle 3 alpha). Variable genes from the Seurat3 analysis of the 12wpc_1 samples (resolution 0.8) were used as input into Monocle, utilizing the VGAM::negbinomial.size expressionFamily, and clusters were projected onto the minimum spanning tree after ordering. The beginning of pseudotime was assigned using the function orderCells based on NGN3 expression.
[0248] To conduct alpha and beta branch analysis, clusters along each branch were isolated and loaded into Monocle separately. Genes that changed significantly as a function of pseudotime were identified with Monocle's differentialGenetest function, and those that displayed a q-value less than 0.001 were selected for downstream analysis. These genes were then plotted as a heatmap (using plot_pseudotime_heatmap) that clustered genes based on similarities in expression patterns along pseudotime. The expression of individual genes was plotted using Monocle's plot_genes_in_pseudotime function.
[0249] For the pseudotemporal ordering analysis of our merged human fetal and hESC-derived cell datasets, we utilized Monocle3 v0.1.0 (named Monocle 3 beta) was used. This version of Monocle3 was used because of its internal batch correction capabilities. For the merged human fetal pseudotemporal ordering analysis, variable genes from the Seurat3 integration analysis were used as input into Monocle. Clusters were projected onto the minimum spanning tree after ordering.
[0250] For the merged hESC-derived analysis, variable genes from CHGA+ sub-clustering were used as input into Monocle. To batch correct based on sample type, the residual_model_formula_str was set to “.sup.˜orig.ident” during the pre-process_cds step. To conduct branch-specific analyses, the choose_cells function was used to manually select the branches of interest in Monocle's graphical user interface. Once branches were selected, genes that changed significantly along pseudotime were identified using the graph_test function. Genes of interest were plotted along the Monocle trajectory using the plot_cells function.
[0251] Genetic Engineering of the FEV-KO hESC Line
[0252] The HUES8 hESC line was used to generate the FEV-KO line. For the FEV-KO hESC line, the FEV-KO gRNA (5′-CTGATCAACATGTACCTGCC-3′; SEQ ID NO:1) was designed on Benchling software and ordered from Dharmacon in a lyophilized format. The gRNA was suspended in nuclease-free 10 mM Tris-HCl Buffer (pH 7.4) ordered from Dharmacon (cat. no. B-006000-100) and stored as aliquots at −80° C. HUES8 hESCs were grown on Matrigel-coated tissue culture plates, and on the morning of nucleofection, media was changed to mTeSR1+10 μM Rock inhibitor Y-27632 for 2 hours prior to nucleofection. Following this incubation step, hESCs were lifted from Matrigel plates and dissociated into a single-cell suspension using TrypLE Express. Cells were incubated in TrypLE Express dissociation reagent for 6 minutes at RT. mTeSR1+10 μM Rock inhibitor Y-27632 was used to neutralize the dissociation, and cell suspension was filtered through a 37 μm filter.
[0253] To carry out the nucleofection, we mixed 2.75 μL of tracrRNA (160 uM) and 2.75 uL of the FEV-KO gRNA (160 μM) (to make the “RNA-complex”) in a PCR strip tube and incubated for 30 minutes in the 37° C. cell culture incubator. After 30 minutes, 5.5 μL of purified Cas9-NLS protein (QB3 UC Berkeley MacroLab) was added to the RNA complex, gently mixed to make the RNP (ribonucleoprotein), and incubated at 37° C. for exactly 15 minutes. After exactly 15 minutes, previously dissociated cells were resuspended in Lonza's P3 buffer from the P3 Primary Cell 4D-Nucleofector X Kit S (V4XP-3032). 10 μl of cell suspension containing 400K cells were pipetted into one well of the Lonza nucleofection strip, and 10 μl of the RNP was added. The nucleofection strip was then inserted into the Lonza 4D-Nucelofector (Lonza, AAF-1002B) and nucleofected with the CA137 setting compatible with the P3 buffer. Nucleofected cells were then transferred to a 15 mL conical with 3 mL of mTeSR1 containing 10 μM Rock inhibitor Y-27632 and pen/strep (penicillin/streptomycin). Cell viability was determined via Moxiflow, and cells were plated in one well of a 6-well plate coated with Matrigel. Cells were grown for 2-3 passages in mTeSR1 containing 10 μM Rock inhibitor Y-27632 and pen/strep to allow for recovery from nucleofection.
[0254] To determine genomic editing efficiency of the FEV-KO nucleofection experiment, genomic DNA from nucleofected cells was harvested in QuickExtract DNA Extraction (Lucigen, QE09050) and then used for PCR amplification. The following forward and reverse primers targeting the FEV-KO editing site were used to produce a 491-bp amplicon: 5′-CCGTCTTCTCCTCCTTGTCACC-3′ (SEQ ID NO:2) and 5′-CTCGGCCACAGAGTACTCCAC-3′ (SEQ ID NO:3). This amplicon is GC-rich, requiring use of a PCR polymerase capable of handling GC-rich amplicons (PrimeSTAR GXL Premix, Clontech). This DNA amplicon and a wild-type DNA amplicon were sent to Quintarabio for Sanger sequencing. The chromatographs of each sequencing run were used for TIDE (Tracking of Indels by Decomposition) analysis, which estimates the frequency of insertions and deletions (indels) in a pool of cells that has undergone genomic editing (Brinkman et al., 2014). Cutting efficiency of hESCs nucleofected with FEV-KO gRNA was then determined.
[0255] To derive a clonal FEV-KO line from this heterogeneous pool of hESCs that have no mutation in the FEV locus, a mutation(s) in one FEV allele, or mutations on both FEV alleles, these cells were clonally plated on Matrigel-coated plates. Approximately 1,500 cells were dispersed onto a 10 cm Matrigel-coated plate and allowed to grow for 9-10 days in mTeSR1. For the first 4-5 days of culture, cells were cultured in mTeSR1 containing 10 μM Rock inhibitor Y-27632. Clonal colonies were then hand-picked under a colony-picking microscope under sterile conditions. These hand-picked colonies were each transferred into one well of a 96-well plate, allowed to grow for 2-3 days, and then successively passaged into large-plate formats (96-well to 24-well to 6-well to 10 cm dish). Clonality was first determined through TIDE analysis, as outlined above, and confirmed with TOPO cloning of the FEV-KO PCR amplicon.
[0256] Genetic Engineering of the FEV-KI hESC Lines
[0257] The HUES8 hESC line was used to generate the FEV-MYC, FEV-GFP, and FEV-tNGFR lines. The MYC, GFP, and tNGFR inserts were all commercially synthesized as gene blocks from Integrated DNA Technologies. 5′ and 3′ FEV locus homology arms that were 400 bp in length were then added to each of the MYC, GFP, and tNGFR gene blocks using In-Fusion HD Cloning (Clontech, 638920). These homology arms flanked the cut site targeted by the FEV-KI gRNA. The result of In-Fusion HD cloning was a pUC19 plasmid containing a MYC, GFP, or tNGFR insert flanked by 5′ and 3′ FEV homology arms. These plasmids were transformed into Stellar Competent Cell (Clontech, 636766), and PCR amplification off of these isolated plasmids generated a PCR amplicon for use as our targeting template to knock in MYC, GFP, and tNGFR into the FEV locus. The following forward and reverse primers were used in PCR to generate each targeting template from each plasmid: 5′-TGAACTACGACAAGCTGAGCCG-3′ (SEQ ID NO:4) and 5′-TCCTTGGGGAAGAGCAAAAGTG-3′ (SEQ ID NO:5).
[0258] For knock-in of MYC, GFP, and tNGFR into the FEV locus, a FEV-KI gRNA (GCCATTACCACTAGACGGGG; SEQ ID NO:6) was designed using Benchling software and targeted the end of exon 3 of the FEV locus. This FEV-KI gRNA cut immediately preceding the FEV stop codon found at the end of exon 3 and would facilitate the knock-in of each insert in-frame with the FEV locus. On the morning of nucleofection, HUES8 hESCs were fed with mTeSR1+10 μM Rock inhibitor Y-27632 for 2 hours. Following this incubation step, hESCs were lifted from Matrigel-coated plates and dissociated into a single-cell suspension using TrypLE Express. Cells were incubated in TrypLE Express dissociation reagent for 6 minutes at RT. mTeSR1+10 μM Rock inhibitor Y-27632 was used to neutralize the dissociation, and cell suspension was filtered through a 37 μm filter.
[0259] To carry out the nucleofection, 1.25 μL of tracrRNA (160 uM), 1.25 μl of FEV-KI gRNA (160 μM), and 1 μg of either the MYC, GFP, or tNGFR targeting templates were mixed in a PCR strip tube and incubated for 30 minutes in a 37° C. cell culture incubator. After 30 minutes, 2.5 μL of purified Cas9-NLS protein (QB3 UC Berkeley MacroLab) was added, gently mixed to make the RNP complex, and incubated at 37° C. for exactly 15 minutes. Dissociated cells were pelleted at 1000 rpm for 3 minutes and resuspended in Lonza P3 buffer (Lonza, V4XP-3032). 10 μL of cell suspension containing 400,000 cells were then pipetted into a Lonza cuvette, and 10 μL of the RNP complex+targeting template was added. The cuvette was then inserted into the Lonza 4D-Nucelofector (Lonza, AAF-1002B) and nucleofected with the CA137 setting compatible with the P3 buffer. Nucleofected cells were then transferred to a 15 mL conical tube with 3 mL of mTESR containing 10 μM Rock inhibitor Y-27632 and pen/strep. Cell viability was determined via Moxiflow, and cells were plated in one well of a 6-well plate coated in Matrigel. Cells were grown for 2 passages to allow for recovery from nucleofection.
[0260] To determine if the MYC, GFP, and tNGFR inserts were successfully knocked-in, genomic DNA from nucleofected cells was harvested in QuickExtract DNA Extraction (Lucigen, QE09050) and used for PCR amplification. The following forward and reverse primers were used: MYC: 5′-AGATCCAGCTGTGGCAGTTTCT-3′ (SEQ ID NO:7) and 5′-ACCAGACAAGGATTGAGGGAGC-3′ (SEQ ID NO:8) GFP: 5′-CGTGCATCTGGAAAGCTACGTG-3′ (SEQ ID NO:9) and 5′-CTTGAAGAAGTCGTGGCGCTTC-3′ (SEQ ID NO:10) tNGFR: 5′-TGAACTACGACAAGCTGAGCCG-3′ (SEQ ID NO:4) and 5′-TCCTTGGGGAAGAGCAAAAGTG-3′ (SEQ ID NO:5). Presence of a knock-in band that was larger than the FEV wild-type band was indicative that a subset of nucleofected cells carried the insert.
[0261] To derive clonal FEV-KI lines from a heterogeneous pool of hESCs that either had the knock-in insert or not, cells were clonally plated on Matrigel-coated plates. Approximately 1,500 cells were dispersed onto a 10 cm Matrigel plate and allowed to grow for 9-10 days in mTeSR1. For the first 4-5 days of culture, cells were cultured in mTeSR1 containing 10 μM Rock inhibitor Y-27632. These hand-picked colonies were each transferred into one well of a 96-well plate, allowed to grow for 2-3 days, and then successively passaged into large-plate formats (96-well to 24-well to 6-well to 10 cm dish). Genomic DNA was isolated from each clonal line and the insert was confirmed through PCR using same primers as indicated above. Sanger sequencing of the genomic FEV locus confirmed that the MYC, GFP, and tNGFR had no mutations and were in-frame with the endogenous FEV locus.
[0262] Generation of Gene KOs During Directed Differentiation of hESCs
[0263] Approximately 100-150×10.sup.6 End Stage 4 (ES4) cells from the directed differentiation protocol were dissociated in Accumax for 15-25 minutes in a 37° C. water bath. The dissociated cell suspension was passed through a 37 μm filter. Cell count and viability were determined with a Moxiflow cell counter. Cells were pelleted at 1000 rpm for 3 minutes and kept in ES4 media until nucleofection.
[0264] For nucleofection, the large format of Lonza's nucleofection kits (P3 Primary Cell 4D-Nucleofector X Kit L, V4XP-3024) was used, which accommodates nucleofection of up to 20×10.sup.6 cells per nucleofection vessel. Four conditions were typically included in these experiments: non-nucleofected control, scramble control, hAAVS1 control, and KO of gene of interest. The non-nucleofected control group contained ES4 cells that did not go through nucleofection. The scramble control group contained ES4 cells that were nucleofected with a scramble gRNA (GGTTCTTGACTACCGTAATT; SEQ ID NO:11) that is not predicted to cut anywhere in the human genome. The hAAVS1 control included ES4 cells that were nucleofected with a gRNA targeting a safe harbor locus in the human genome AAVS1 (GGGGCCACTAGGGACAGGAT; SEQ ID NO:12. The KO of gene of interest group contained ES4 cells that were nucleofected with a gRNA targeting the gene of interest we wished to knock out. All gRNAs were ordered from Dharmacon.
[0265] For each nucleofection set of 10-20×10.sup.6 ES4 cells, 9.5 μL of tracrRNA (160 μM), 9.5 μL of the FEV-KI gRNA (160 μM) were mixed in a PCR tube and incubated for 30 minutes in a 37° C. cell culture incubator. After 30 minutes, 19 μL of purified Cas9-NLS protein (QB3 UC Berkeley MacroLab) was added, gently mixed to make the RNP complex, and incubated at 37° C. for exactly 15 minutes. Dissociated ES4 cells were pelleted at 1000 rpm for 3 minutes, and each set of 10-20×10.sup.6 ES4 cells were resuspended in 64 μL of Lonza P3 buffer (from V4XP-3024). Each set of cells were then pipetted into a large Lonza nucleofection vessel, and 36 μl of the RNP were added.
[0266] Nucleofection vessel was then inserted into the Lonza 4D-Nucleofector (Lonza, AAF-1002B) and nucleofected with the CA137 setting compatible with the P3 buffer. Nucleofected cells were then transferred to a 15 mL conical tube with 10 mL of S5D1 media. Cell viability was determined via Moxiflow.
[0267] Following nucleofection, cells were immediately re-aggregated into clusters using AggreWell 400 plates (STEMCELL Technologies, 34415). Wells in the AggreWell 400 plates were washed with an Anti-Adherence Rinsing Solutions (STEMCELL Technologies, 07010) and centrifuged in a swinging bucket rotor at 1300×g for 5 minutes. Rinsing solution was removed, and S5D1 media was used to rinse wells. S5D1 media was aspirated, and 1.2×10.sup.6 cells were then pipetted into each well of an AggreWell 400 plate. Plates were spun at 100×g for 3 minutes to facilitate re-aggregation of cells in each microwell and then were observed under microscope to verify even distribution of cells among microwells. Plates were placed in the 37° C. cell culture incubator, and spheroids formed by 48 hours (by S5D3). On S5D3, clusters were removed from the AggreWell plates and cultured in either miniature spinner flasks called Biotts (BWV-503A) set at a 70 rpm rotation speed or in 6-well ultra low-attachment plates (5 mL of media with approximately 5×10.sup.6 cells per well) placed on an orbital shaker set to 100 rpm. Directed differentiation of these nucleofected clusters was continued either in Biotts or in a 6-well ultra low-attachment plate.
[0268] FACS of hESC-Derived Cells
[0269] BLC clusters were dissociated in Accumax for 15-25 minutes in a 37° C. water bath. The dissociated cell suspension was passed through a 37 μm filter. Cells were pelleted at 1000 rpm for 3 minutes and fixed in 4% PFA for 12 minutes at RT. Cells were washed in 1×PBS, pelleted again, and resuspended in 1×PBS. Fixed cells were stored at 4° C. prior to staining for FACS.
[0270] For FACS staining, cells were permeabilized using 1× Permeabilization Buffer (Invitrogen, 00-8333-56) for 5 minutes at RT. Cells were then incubated in primary antibody diluted in Blocking reagent (0.2% Triton X-100, 5% NDS, 1% Bovine Serum Albumin (BSA) in 1×PBS) overnight at 4° C. Primary antibodies used were anti-Chromogranin A (1:500, Abcam ab15160) and anti-C-Peptide (1:200, EMD Millipore 05-1109). The next day, cells were washed in 1× Permeabilization Buffer for 5 minutes at RT and incubated in species-specific Alexa Fluor 488- and 555-conjugated secondary antibodies (1:500, Jackson ImmunoResearch) for 30 minutes at RT. Cells were then washed in 1× Permeabilization Buffer, pelleted, resuspended in 1×PBS, and analyzed with BD Fortessa Analyzer.
Example 9
[0271] Diversity of Cell Types in the Developing Human Pancreas
[0272] Improving the ability to generate terminally differentiated cell types in all animals, including humans, will be beneficial in providing higher quality healthcare, at lower cost, and also in improving the quality of life for many. Gaining a better understanding of the cell stages required for human endocrine cell development as well as the transcriptional circuitry driving lineage allocation into distinct endocrine linages will refine our ability to generate these hormone-expressing cell types from human embryonic stem cells.
[0273] The discovery of a novel endocrine progenitor defined by high Fev expression in mouse, as disclosed herein, prompted the question of whether additional endocrine progenitor stages exist in human endocrine cell development beyond the NGN3+ endocrine progenitor stage. Defining these stages in humans can be leveraged to more properly mimic human endocrine cell development in vitro during directed differentiation protocols that harness the power of hESCs.
[0274] The different cellular compartments and their transcriptional profiles from human fetal pancreas were characterized. The focus was on a 12wpc time point, which represents a period of peak NGN3 expression and active cell differentiation in the developing human pancreas (Nair and Hebrok, 2015). Tissue from this 12wpc time point was dissociated into a single-cell suspension, and RBCs were removed via immunomagnetic separation. The resulting single-cell suspension was loaded onto two wells of the 10× Chromium Single-Cell Platform and prepared for sequencing using version 3 (V3) chemistry. Following sequencing and de-multiplexing of single-cell data, UMAP-based clustering of merged well replicates revealed 22 cell clusters organized into acinar, ductal, endocrine, mesenchymal, endothelial, immune, and nerve populations based on the expression of known marker genes, such as CPA1 (acinar), SOX9 (ductal), CHGA (endocrine), COL1A1 (mesenchymal), PECAM1 (endothelial), PTPRC (immune), and SOX10 (nerves) (
Example 10
[0275] Identification of Novel Cell Stages During Human Endocrine Cell Development
[0276] Given our previous identification of novel progenitor stages in mouse pancreatic development, we next focused on the endocrine compartment of the developing human pancreas in order to determine if additional endocrine stages exist beyond those characterized by NGN3+ progenitors and differentiated hormone+ endocrine cells. Sub-clustering of the CHGA+ cell clusters resulted in increased resolution of the endocrine lineage populations present in 12wpc human fetal pancreas, revealing 11 distinct endocrine lineage populations (
[0277] Three additional cell clusters (clusters 6, 8, and 9) that were devoid of any hormone expression (
[0278] The inclusion of other hormone-expressing endocrine lineages in pseudotemporal ordering did not result in a continuous differentiation trajectory (
[0279] Pairwise comparisons and examination of the top differentially expressed genes of clusters 6, 8, and 9 revealed that these populations represent novel cell stages of human endocrine cell differentiation at a resolution that we have not been able to appreciate with previous techniques. NGN3 expression was concentrated within the common endocrine progenitor population (cluster 6), although NGN3 was not among the top 5 differentially expressed genes (
Example 11
[0280] Candidate Lineage Regulators of the Beta Cell Lineage
[0281] The onset of NGN3 expression marks the beginning of endocrine cell development as cells differentiate towards a hormone+ endocrine lineage. However, the transcriptional programs that guide these endocrine progenitors toward a distinct hormone-expressing endocrine lineage are not well defined in human endocrine cell differentiation. With the lack of lineage tracing tools available for in vivo human studies, single-cell RNA-sequencing data was used to make inferences about the transcriptional machinery that regulates endocrine lineage allocation. Given that we observed distinct stages of cellular differentiation leading to both alpha and beta lineages (
[0282] Pseudotemporal ordering was used to identify genes that were differentially expressed across a single-cell trajectory. This analysis was first applied to the beta lineage branch, which exhibited differentiation starting with cluster 6 cells (common endocrine progenitors) to cluster 8 cells (pre-beta progenitors) and finally to beta cells (
[0283] In view of the data, it was expected that genes within gene clusters 1, 6, and 7, which were upregulated during the pre-beta progenitor stage, would serve as regulators of beta cell lineage allocation. FEV was found in gene cluster 7. Genes that displayed high upregulation during the pre-beta progenitor stage also included genes known to be involved in beta cell differentiation and function, such as CHGB, SCG5, ERO1B, MAFB, and PAX6 (
[0284] We also identified candidate regulators not previously known to be involved in beta cell lineage allocation. These genes were organized into three broad categories: those that were imprinted, those involved in neural development, and others involved in transcription and canonical signaling pathways. Imprinted genes that were upregulated during beta cell differentiation included DLK1, MEG3, GNAS, PLAGL1, PEG3, and PEG10 (
[0285] Upregulated genes known to be involved in neural development included ASCL2, AHI1, and SEZ6L2 (
Example 12
[0286] Candidate Lineage Regulators of the Alpha Cell Lineage
[0287] A similar analysis was applied to identify genes that were differentially expressed during differentiation into alpha cells. Based on pseudotemporal ordering, differentiation of the alpha lineage from endocrine progenitors began with cluster 6 (common progenitors) that differentiated into cluster 9 cells (pre-alpha progenitors), which then became differentiated alpha cells found in cluster 9 (
[0288] Given that gene clusters 4 and 5 contained genes that were upregulated specifically after NGN3 downregulation and the acquisition of alpha cell identity, these genes were expected to be regulators of alpha lineage allocation. Similar to the analysis of the beta cell lineage, MAFB, PAX6, ERO1B, AHI1, PEG10, SCG5, and ACVR1C were found to be upregulated along alpha cell differentiation, indicating that these markers are common genes upregulated during endocrine cell differentiation as a whole. We observed upregulation of various neural transcription factors and genes during alpha cell differentiation. BEX2, BEX4, and BEX5 were all upregulated during alpha cell fate allocation and are members of the brain-expressed X-linked transcription factor family that are highly expressed in the brain (Alvarez et al., 2005) (
Example 13
[0289] Cellular and Transcriptional Dynamics of the Developing Endocrine Compartment
[0290] To understand endocrine cell development across actual developmental time, single-cell RNA-sequencing was performed on tissues at three additional time points to add to the analysis on 12wpc human fetal pancreas (referred to as 12wpc_1): a second biological replicate of 12wpc (referred to as 12wpc_2), 15.5wpc, and 16wpc. All datasets were depleted of red blood cells through immunomagnetic separation and were generated using the 10× Genomics version 3 (V3) sequencing chemistry, except for the 15.5wpc sample, which was enriched for EPCAM+ cells through FACS and processed using version 2 (V2) sequencing chemistry. All datasets were merged with Seurat 3's new integration method for merging and batch correction, resulting in 31 distinct clusters (
[0291] Sub-clustering of the endocrine lineage resulted in 15 distinct populations (
[0292] We next sought to reconstruct lineage relationships across multiple time points through pseudotemporal ordering. Batch effect, unfortunately, is a major issue that still confounds single-cell RNA-sequencing analysis, despite multiple groups developing algorithms to address this problem (Butler et al., 2018; Haghverdi et al., 2018; Stuart et al., 2019). In our merged dataset, batch effect became problematic as our 15.5wpc sample was processed with V2 10× Genomics version chemistry as opposed to V3 chemistry, which was utilized for processing the 12wpc and 16wpc samples. V3 chemistry increased the sensitivity of gene capture, and this was particularly evident by the percentage of mitochondrial genes captured in V3 datasets, in which more mitochondrial genes were represented (
Example 14
[0293] Understanding the Emergence of Distinct Cellular Compartments During In Vitro Beta Cell Differentiation at Single-Cell Resolution
[0294] Directed differentiation of hESCs to a beta cell lineage represents a powerful approach for not only generating beta cells for diabetes but also understanding human beta cell differentiation. Given the significant heterogeneity in cells generated by directed differentiation of hESCs towards the beta lineage, single-cell RNA-sequencing was leveraged to classify distinct cellular populations that arose across five main stages of in vitro beta cell differentiation: stages containing early-, middle-, and late-stage endocrine progenitors (ES4, S5D4, and S5D7) and two stages within the beta lineage stage (S6D4 and S6D10). UMAP-based clustering of all five time points revealed the presence of PDX1+ clusters, reflecting induction towards the pancreatic lineage during the directed differentiation towards the beta lineage (
Example 15
[0295] hESC-Derived FEV+ Cells are Transcriptionally Similar to In Vivo FEV+ Progenitors
[0296] Given that we had identified an endocrine progenitor stage defined by FEV expression in both mouse (Byrnes et al., 2018) and human fetal beta cell development (
Example 16
[0297] Mapping In Vitro Beta Cell Differentiation at Single-Cell Resolution
[0298] The lineage relationships among hESC-derived endocrine cells during in vitro beta cell differentiation were also reconstructed. First, all CHGA+ endocrine clusters from each sampled time point were merged using Seurat 3 (
Example 17
[0299] FEV Appears to be Required for Proper Human Beta Cell Differentiation and Function
[0300] In addition to identifying FEV as a marker for endocrine progenitor stages in human endocrine cell development, we wanted to determine if FEV had any functional role in beta cell differentiation. In Fev knockout (KO) mice, glucose clearance from the blood following a glucose challenge was significantly slowed, and the insulin content of beta cells was decreased (Ohta et al., 2011). Given that this study utilized a whole-body Fev KO, the defects in glucose homeostasis and the reduction in insulin content could have been a result of a requirement for FEV in non-pancreas cells or for FEV function in multiple stages in the lifetime of a beta cell. To test the requirement of FEV in human beta cell differentiation and function, the in vitro beta cell differentiation platform was used to first generate a FEV-KO hESC line through CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9-mediated genomic editing (
Example 18
[0301] Generation of New Tools and Platforms for Understanding Human Beta Cell Development: Identification of FEV Transcriptional Targets, Isolation of FEV-Expressing Cells During In Vitro Beta Cell Differentiation, and Validation of Novel Candidate Regulators of Beta Cell Lineage Allocation and Function
[0302] The discovery that the transcription factor FEV is expressed in hESC-derived endocrine progenitors and beta-like cells prompted us to generate tools through which we could interrogate the function of the FEV gene. Through CRISPR/Cas9-mediated genomic editing, a FEV-MYC hESC line was constructed in which a MYC epitope tag was fused to the endogenous FEV transcription factor at the C-terminus (
[0303] Given that FEV is a transcription factor that is required for proper endocrine differentiation in an in vitro beta cell differentiation platform, the generation of this FEV-MYC hESC line is expected to be valuable in interrogating the mechanism through which FEV regulates proper human beta cell differentiation. ChIP-seq on FEV+ endocrine progenitors at Stage 5 of our in vitro differentiation can identify transcriptional targets of FEV (
[0304] As disclosed herein, tools to isolate and characterize the FEV-expressing cell population during human beta cell differentiation have been developed. Two FEV reporter hESC lines have been constructed: a FEV-GFP line and a FEV-tNGFR (truncated Nerve Growth Factor Receptor) line (
[0305] Purification of FEV-expressing cells at defined stages of the in vitro beta cell differentiation process will enable us to understand the differences among FEV-expressing populations at each differentiation stage. Specifically, purifying FEV+ endocrine progenitors at stage 5 of our differentiation program will permit small molecule screens to identify compounds that can either induce progenitor expansion prior to beta cell lineage commitment or enhance differentiation toward the beta lineage (
[0306] The in vivo and in vitro single-cell RNA-sequencing analyses have resulted in the identification of candidate beta cell lineage regulators. In order to functionally validate these candidate regulators, we developed a flexible platform on which we can test whether these genes regulate beta cell lineage allocation (
[0307] The preceding Examples establish a platform and methodologies useful in promoting and optimizing protocols for directing progenitor cells into a differentiation pathway leading to mature, functional alpha and beta cells. The present disclosure contains several discrete advances useful in achieving these goals.
[0308] Redefining the NGN3+ Endocrine Progenitor Population in Human Pancreatic Development
[0309] In human endocrine cell development, NGN3 has long been thought to mark the endocrine progenitor population, given the function of Ngn3 in mouse pancreatic development. Indeed, NGN3 is required for endocrine cell differentiation in human endocrine cell development, as inactivating mutations of NGN3 lead to neonatal diabetes (Pinney et al., 2011; Wang et al., 2006). Beta cell mass is suspected to be reduced, not absent, in human cases of inactivating NGN3 mutations given that C-peptide is detected in the blood, albeit at low levels (Pinney et al., 2011). This is in contrast to mouse development, in which NGN3 ablation halts beta cell generation altogether (Gradwohl et al., 2000). Through the studies of human endocrine cell development disclosed herein, NGN3 did not appear to be the most robust marker of the endocrine progenitor population common to hormone-expressing lineages, such as the alpha and beta lineages, in our 12wpc_1 human fetal pancreas dataset. Other markers that appeared to more faithfully label this common endocrine progenitor population included EMC10, SOX4, HES6, and KRT19. CTD-2545M3.8 also emerged from our differential gene expression analysis as a marker specific to this common endocrine progenitor population, but awaits functional characterization.
[0310] In murine endocrine development, Ngn3+ endocrine progenitors give rise to all five hormone-expressing lineages of the pancreas (Gradwohl et al., 2000; Heller et al., 2005). However, in the human fetal pancreas, the NGN3-expressing common endocrine progenitor population appeared to only give rise to alpha and beta lineages. In our pseudotemporal ordering analysis, there was no trajectory that connected NGN3-expressing progenitors to the SST-expressing delta lineage or the GHRL-expressing epsilon lineages. A distinct PPY-expressing gamma population was not observed, as all PPY-expressing cells also expressed GCG and thus were annotated as alpha cells. If the difference in differentiation potential between human and mouse cells reflects true lineage relationships of NGN3-expressing endocrine progenitors in the developing human pancreas, this would depart from the dogma established by findings of the lineage potential of Ngn3+ progenitors in murine pancreatic development.
[0311] Identification of Novel Pre-Alpha and Pre-Beta Cell Stages in Human Pancreatic Development
[0312] Mapping endocrine cell development at a higher resolution using single-cell RNA-sequencing can be leveraged for developing new methods to generate endocrine cell types more efficiently from stem cell sources. Disclosed herein is the identification of pre-alpha and pre-beta progenitor stages that provide increased resolution regarding the steps required to differentiate into alpha or beta lineages in human pancreatic development. The work disclosed herein on human fetal pancreatic development offers novel endocrine progenitor stages onto which we can compare and contrast the biological relevance of the murine progenitor stages to those of human. The advent of single-cell RNA-sequencing has led to the discovery of several endocrine progenitor stages in mouse pancreatic development. One example is the work disclosed herein, which reveals an intermediate endocrine progenitor population defined by high Fev expression. Fev expression has also been identified in endocrine progenitor populations reported by several other single-cell RNA-sequencing studies of murine pancreatic development (Krentz et al., 2018; Scavuzzo et al., 2018), confirming the reproducibility of our finding. This Fev+ endocrine progenitor is derived from a Ngn3.sup.+ population, and differentiated endocrine lineages in the murine pancreas transit through a Fev− expressing cell stage (Byrnes et al., 2018).
[0313] Within the Fev+ progenitor population, cells that appeared to be pre-specified towards an alpha or beta cell fate were found. This is analogous to human pancreatic development in which we not only identified endocrine progenitors that expressed FEV but also observed that these FEV-expressing progenitors appeared to be already lineage-specified towards an alpha or beta cell fate. The in silico reconstruction of endocrine lineage relationships indicated that endocrine cell fate decisions in progenitors occurs at the Fev/FEV-expressing cell stage in both mouse and human.
[0314] Beyond this Fev-expressing endocrine progenitor stage, there are additional endocrine progenitor stages that have been identified in murine development. In particular, four distinct endocrine progenitor stages (termed EP1-4) have been proposed in mouse endocrine cell development (Yu et al., 2019). Expression of Ngn3, the canonical pro-endocrine lineage marker in pancreatic development, increased in EP1, peaked in EP2, decreased in EP3, and was not observed in EP4. Expression of Fev was found in EP3 and EP4 stages only (Yu et al., 2019), which is concordant with Fev being downstream of Ngn3 (Byrnes et al., 2018; Miyatsuka et al., 2014). Interestingly, many of the differentially expressed genes in each EP stage were also identified as top differentially expressed genes in either our human endocrine progenitor clusters or during pseudotemporal ordering. Specifically, Krt19 and Gadd45a, two genes that defined a human common endocrine progenitor stage in our dataset of 12wpc_1 human fetal pancreas, were found to be differentially expressed in EP2. Several candidate beta lineage regulators in human fetal development were also found in EP1 (Arid5b), EP3 (Ahi1), and EP4 (Rbp4, Peg10, Acvr1c, Sez6l2) (Yu et al., 2019). Similarly, several candidate alpha lineage regulators in human fetal development were found in EP3 and EP4 (Arx, Irx2, Fam46a, Slc30a8, Slc7a2, Slc7a8, Cryba2, St18, and Alcam) (Yu et al., 2019). Thus, these EP stages found in murine endocrine development appear to also have relevance to endocrine progenitor stages found in human fetal pancreatic development.
[0315] Transcriptional Mechanisms Underlying Fate Decisions are Shared Across Tissues
[0316] The single-cell RNA-sequencing analysis of human endocrine lineage allocation identified many candidate regulators previously identified and studied in the nervous system. Despite their derivation from different germ layers, both the pancreatic endocrine and neural lineages employ many of the same transcription factors that regulate their own development, including Ngn3, NeuroD1, Nkx2.2, Nkx6.1, Pax family of transcriptional regulators, and Fev (Blake and Ziman, 2014; Churchill et al., 2017; Gradwohl et al., 2000; Hendricks et al., 1999; Mastracci et al., 2013; Napolitano et al., 2015; Ohta et al., 2011; Pataskar et al., 2016; Prakash et al., 2009; Qi et al., 2001; Schaffer et al., 2010; Simon-Areces et al., 2010; St-Onge et al., 1997). These transcriptional similarities have an evolutionary basis, as the main source of insulin in invertebrates is in neurons (Wong et al., 2014). Thus, from an evolutionary perspective, it is not surprising that additional genes previously identified to be required for proper nervous system development and function are also implicated in pancreatic endocrine development and, more specifically, lineage allocation.
[0317] The development of enteroendocrine cells (EEs) in the intestine also shares striking similarity to pancreatic endocrine cell development. Proper differentiation of EEs in the intestine during development requires transcription factors also critical for pancreatic endocrine cell differentiation, including Ngn3 (Jenny et al., 2002; López-Díaz et al., 2007; Schonhoff et al., 2004), Nkx2.2 (Gross et al., 2016), Isl1 (Terry et al., 2014), NeuroD1 (Naya et al., 1997), Pax4 (Beucher et al., 2012a). As in pancreatic endocrine cell development, the EE lineage comprises multiple hormone-expressing cell types that are derived from a common progenitor cell defined by Ngn3 (Jenny et al., 2002). Recent work applying single-cell RNA-sequencing to murine EE development uncovered novel markers and lineage-specific regulators of the multiple EE lineages (Gehart et al., 2019), and many of these genes overlapped with the markers and candidate transcriptional regulators that we identified in mouse and human endocrine cell development and lineage allocation. Ngn3+ EE progenitors differentially express Sox4, Tox3, and Gadd45a (Gehart et al., 2019), all of which were also defining markers of our common endocrine progenitor in human endocrine cell development. Known hormone-specific lineage regulators in pancreatic endocrine cell development, such as Arx, Pax6, and Isl1, were also identified as EE-specific lineage regulators (Gehart et al., 2019). Interestingly, a number of novel candidate lineage regulators that we identified in mouse and human endocrine lineage allocation were also found to be lineage-specific regulators of the different EE lineages (Gehart et al., 2019). These include Nr4a2, Smarca1, Peg3, In, S100a1, and Klf4 (Gehart et al., 2019).
[0318] Timing of Endocrine Lineage Fate Decisions
[0319] The timing of endocrine lineage fate commitment in pancreatic development is not fully understood. The Ngn3+ endocrine progenitor stage has long been regarded as the master stage prior to endocrine cell differentiation, but the single-cell RNA-sequencing studies of pancreatic development disclosed herein have identified additional progenitor stages that arise between initial Ngn3 expression and acquisition of differentiated cell identity. This increased resolution of endocrine cell differentiation has provided us with new cell stages that we can interrogate for determining when endocrine lineage decisions are made. From both the mouse and human studies of endocrine cell development disclosed herein, Fev/FEV-expressing endocrine progenitors were already specified towards an alpha or beta cell fate. This heterogeneity in Fev/FEV-expressing progenitors suggests that endocrine lineage specification occurs at or before this Fev/FEV-expressing progenitor stage. The single-cell RNA-sequencing combined with pseudotemporal ordering identified endocrine progenitor populations that appeared to be fated towards one specific endocrine lineage.
[0320] The timing of endocrine fate decisions can also be regulated by extrinsic signals derived from the surrounding microenvironment. In murine development, the developmental time at which Ngn3+ progenitors form corresponds to their ultimate hormone lineage selection (Johansson et al., 2007). The competence window for alpha differentiation occurs earliest in murine pancreatic development, resulting in alpha cells being the first emerging endocrine lineage, followed by beta and gamma cells, and then lastly followed by delta cells (Johansson et al., 2007). In contrast, in human pancreatic development, the beta lineage is the earliest endocrine cell type to be detected (at 6wpc), followed by alpha cells (at 8-9wpc), delta cells (10wpc), and gamma cells (at 17wpc) (Jeon et al., 2009; Piper et al., 2004). Without wishing to be bound by theory, the differences in timing of emergence of endocrine lineages between mouse and human could be a direct result of the changing microenvironment during development that can be providing dynamic cues that promote one endocrine lineage over the other. From murine studies, we know that several compartments of the microenvironment influence pancreatic development, including vasculature, nerves, and mesenchyme (Borden et al., 2013; Golosow and Grobstein, 1962; Landsman et al., 2011; Magenheim et al., 2011; Reinert et al., 2013). However, the cellular composition of each microenvironment compartment can widely differ between that of mouse and human. From the single-cell profiling of human fetal pancreas provided herein, we identified several populations of endothelial cells whose transcriptional expression profiles changed throughout the course of development. These changes may influence the competency of endocrine progenitors to differentiate into distinct hormone lineages, either through secreted signaling molecules or direct interactions. Our single-cell profiling in both mouse and human pancreatic development also reflects different mesenchymal and nerve populations whose dynamics may regulate endocrine differentiation at distinct periods in development.
[0321] FEV in Human Endocrine Cell Differentiation and Function
[0322] Disclosed herein is an investigation of the role of FEV in human endocrine cell differentiation and function, which highlights potential differences between Fev/FEV in mouse versus human. In both mouse and human endocrine cell development, Fev/FEV was expressed in an intermediate progenitor stage that followed initial NGN3 expression and preceded hormone acquisition (Byrnes et al., 2018). FEV was also expressed in endocrine progenitor stage cells during in vitro beta cell differentiation from hESCs, which was concordant with FEV expression in endocrine progenitors in human fetal pancreatic development. Notably, although Fev-KO mice do not exhibit obvious differentiation defects in the islet lineages during development, we did observe a reduction in the differentiation into CHGA+/CPEP+ beta cells in the in vitro beta cell differentiation model. This indicates that FEV is required for human beta cell differentiation and is dispensable for mouse beta cell differentiation. Notable differences were also observed between Fev/FEV in mouse and human differentiated endocrine cells. While Fev expression persists in the alpha and beta lineages during mouse pancreatic development, FEV expression was downregulated in beta cells and only maintained in the alpha lineage in human pancreatic development. Single-cell RNA-sequencing of adult human islets has indicated that FEV is expressed in alpha cells and not beta cells (Segerstolpe et al., 2016). This is in contrast to the in vitro beta cell differentiation system, in which beta cells maintained FEV expression following differentiation of the FEV+ endocrine progenitor stage. In mouse beta cells, FEV binds to the insulin promoter to regulate Insulin transcription and, thus, insulin production. Given that FEV turns off in differentiated human beta cells in vivo, it is possible that FEV is either not needed for beta cell function or FEV inhibits beta cell function. In the in vitro beta cell differentiation system, we observed a subset of INS+ beta cells that did not express FEV, whereas another subset of INS+ beta cells did express FEV. The INS+/FEV− hESC-derived beta cells may correspond to bona fide beta cells found in vivo, and the INS+/FEV+ hESC-derived beta cells may either be mis-differentiated or on their way towards a FEV− state.
[0323] Identification of FEV transcriptional targets provides a clearer picture of its function. In the human beta cell lineage, loss of FEV coincided with a reduction in beta cell differentiation. Given that FEV was expressed in pre-beta progenitors in vivo and hESC-derived endocrine progenitor stage cells, FEV is expected to serve as a key transcriptional regulator for differentiation from a progenitor to a beta cell. Using the FEV-MYC hESC line during in vitro beta cell differentiation and performing ChIP-seq on FEV+ endocrine progenitor stage cells identified transcriptional targets expected to mediate the transition from a pre-beta progenitor to a differentiated beta cell. A transcriptional map of FEV transcription factor activity enables modification of current in vitro beta cell differentiation protocols to one that promotes the expression of key FEV-regulated transcriptional circuits that promote beta cell differentiation from endocrine progenitors. Identification of transcriptional targets in hESC-derived FEV+ beta cells also illuminated the function of FEV in differentiated beta cells.
[0324] Suppressing the Formation of hESC-Derived Blocked Endocrine Progenitors
[0325] Tremendous effort has been devoted to determining the molecular cues that will make derivation of the beta lineage from hESCs more efficient. Disclosed herein is a hESC-derived FEV-expressing population that appeared to be mis-differentiated during in vitro beta cell differentiation. The top differentially-expressed gene of this blocked, FEV-expressing cell population was the transcription factor PHOX2A. Interestingly, PHOX2A was also previously reported to mark a non-endocrine population that emerged in in vitro beta cell differentiation but was not described as mis-directed in differentiation potential (Veres et al., 2019). PHOX2A is a pro-neural homeodomain transcription factor and a key regulator of neural progenitor differentiation into noradrenergic neurons of the central nervous system (CNS) and the peripheral nervous system (PNS) (Lo et al., 1998; Morin et al., 1997). Noradrenergic neurons are characterized by synthesis and storage of catecholamines, including norepinephrine, which serve as neurotransmitters (Hayashida and Eisenach, 2018). In this differentiation process, BMP2 and cyclic AMP (cAMP) signaling synergistically induce noradrenergic neuron differentiation through Phox2a transcription and Phox2a activation (Benjanirut et al., 2006; Chen et al., 2005; Paris et al., 2006). Given the expression of PHOX2A specifically in cells occupying the mis-differentiated trajectory in the pseudotemporal ordering analysis disclosed herein, it is expected that the PDX1+/NKX6.1+ pancreatic progenitors in the in vitro beta cell differentiation process have mis-differentiated towards this PHOX2A+ noradrenergic neural lineage.
[0326] Inhibition of BMP2 and cAMP signaling represents possible avenues through which in vitro beta cell differentiation can avoid entering this mis-directed differentiation path that resembled the noradrenergic neural lineage.
[0327] Reported Enterochromaffin Cells in In Vitro Beta Cell Differentiation
[0328] Currently, in vitro beta cell differentiation does not result in 100% purity of beta cells, and there are other cell types that arise during the directed differentiation of hESCs to the beta lineage. Recently, a population deemed enterochromaffin cells (ECs) has been described as arising in in vitro beta cell differentiation (Veres et al., 2019). ECs reside along the epithelial lining of the intestine and are the most abundant cell type among the enteroendocrine cells found in the intestine (Lund et al., 2018). The main functions of ECs are to regulate intestinal motility required for digestion and modulate the activity of the enteric nervous system through the production and secretion of the neurotransmitter serotonin. Although ECs make up less than 1% of the total intestinal epithelium, they produce more than 90% of the body's serotonin (Gershon, 2013; Mawe and Hoffman, 2013). Unlike neurons, ECs utilize tryptophan hydroxylase 1 (TPH1) and not TPH2 to synthesize serotonin, and instead of employing small neurosecretory vesicles, ECs store serotonin in large dense core vesicles (LDCVs) with the help of CHGA and CHGAB (Cote et al., 2003; Machado et al., 2010; Walther and Bader, 2003). Thus, ECs resemble lineages of both the nervous system and hormone-secreting pancreatic islets.
[0329] ECs are defined by the expression of markers that also are expressed by both serotonergic neurons and pancreatic endocrine cells. These markers include Fev, Lmx1a, Lmx1b, and Tph1 (Ding et al., 2003; Kiyasova and Gaspar, 2011; Liu et al., 2010; Maurer et al., 2004; Ohta et al., 2011; Wyler et al., 2016; Zhang et al., 2017). Proper differentiation of ECs in the intestine during development also requires transcription factors critical for pancreatic endocrine cell differentiation, including Ngn3 (Jenny et al., 2002; López-Díaz et al., 2007; Schonhoff et al., 2004), Nkx2.2 (Gross et al., 2016), Isl1 (Terry et al., 2014), NeuroD1 (Naya et al., 1997), Pax4 (Beucher et al., 2012a). Interestingly, Fev is also expressed by ECs but is not required for EC differentiation in mice (Wang et al., 2010b). Given the striking similarity in gene expression profiles of ECs and EC differentiation to those of pancreatic endocrine cells, it is not surprising to observe ECs generated in in vitro beta cell differentiation. The mis-differentiation of hESC-derived endocrine progenitors towards similar lineages, such as that of the EC, is not surprising, given that in vitro beta cell differentiation is not 100% efficient. It is likely that the ECs observed during in vitro beta cell differentiation represent another mis-differentiation process, similar to that observed with mis-differentiated PHOX2A+ cells. Given that these hESC-derived ECs also express FEV, these ECs could be mistaken for the FEV+ endocrine progenitors identified in human pancreatic development. However, this is not the case, given that a separate population of FEV+ hESC-derived endocrine progenitors is found that give rise to the beta lineage in the in vitro differentiation process disclosed herein. It is likely that these FEV+ ECs are derived from the same hESC-derived FEV+ endocrine progenitors that gave rise to beta cells.
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[0599] All publications and patents mentioned in the application are herein incorporated by reference in their entireties or in relevant part, as would be apparent from context. Various modifications and variations of the disclosed subject matter will be apparent to those skilled in the art without departing from the scope and spirit of the disclosure. Although the disclosure has been described in connection with specific embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Various modifications of the described modes for making or using the disclosed subject matter that are obvious to those skilled in the relevant field(s) are intended to be within the scope of the following claims.
APPENDIX
[0600]
TABLE-US-00002 TABLE 1 SEURAT BIMODAL FOLD LIKELIHOOD CHANGE RATIO WILCOXON MAST CLUSTER ADJUSTED ADJUSTED ADJUSTED GENE NAME X VS. ALL P-VALUE P-VALUE P-VALUE CLUSTER ID SPP1 26.54 1.09E−36 1.78E−45 6.53E−36 NGN3+/SPP1+ SERPINA6 6.54 1.15E−25 2.99E−22 8.33E−21 NGN3+/SPP1+ TMEM171 6.23 7.57E−22 1.63E−41 9.20E−22 NGN3+/SPP1+ MT2 6.11 4.20E−15 5.47E−24 1.20E−13 NGN3+/SPP1+ ID1 5.54 1.57E−16 9.57E−21 7.54E−16 NGN3+/SPP1+ MT1 4.99 8.49E−16 3.55E−14 1.33E−15 NGN3+/SPP1+ H19 4.99 1.72E−25 1.78E−23 2.65E−26 NGN3+/SPP1+ CLU 4.72 3.22E−20 2.57E−16 1.49E−19 NGN3+/SPP1+ PHLDA1 4.72 8.58E−21 7.12E−17 2.31E−19 NGN3+/SPP1+ ALDH1B1 4.59 6.73E−22 4.61E−35 3.02E−22 NGN3+/SPP1+ CLPS 4.44 4.00E−08 7.60E−07 1.85E−06 NGN3+/SPP1+ RASGRP3 4.38 6.80E−13 6.61E−24 3.84E−10 NGN3+/SPP1+ TINAGL1 4.32 3.44E−14 1.23E−28 1.32E−12 NGN3+/SPP1+ DBI 4.26 2.46E−25 7.53E−22 4.94E−24 NGN3+/SPP1+ WFDC2 4.20 4.57E−18 1.66E−23 1.46E−17 NGN3+/SPP1+ CXCL12 3.97 9.42E−15 5.63E−18 3.27E−12 NGN3+/SPP1+ SPARC 3.97 3.48E−15 1.60E−19 1.03E−14 NGN3+/SPP1+ CPA1 3.89 4.65E−10 1 1 NGN3+/SPP1+ LDHA 3.86 4.65E−15 6.42E−13 1.42E−11 NGN3+/SPP1+ CPA2 3.84 1.95E−12 1 1 NGN3+/SPP1+ CD24A 3.78 1.21E−14 1.58E−14 1.23E−14 NGN3+/SPP1+ MGST1 3.78 2.81E−17 4.47E−29 8.60E−18 NGN3+/SPP1+ IFITM3 3.73 4.05E−16 1.32E−25 6.10E−15 NGN3+/SPP1+ CAT 3.53 2.07E−12 2.37E−17 1.97E−11 NGN3+/SPP1+ GADD45A 3.48 2.34E−11 2.66E−06 1.18E−07 NGN3+/SPP1+ SOX4 3.46 8.28E−15 2.35E−13 7.27E−15 NGN3+/SPP1+ RBP1 3.29 3.13E−11 2.79E−13 3.15E−10 NGN3+/SPP1+ HES1 3.07 6.17E−09 2.57E−17 8.66E−10 NGN3+/SPP1+ NEUROG3 3.07 1.32E−12 1.00E−10 4.65E−09 NGN3+/SPP1+ AMOTL2 3.05 3.10E−12 1.44E−20 4.12E−12 NGN3+/SPP1+ SERPINH1 3.01 5.59E−16 1.91E−13 5.27E−16 NGN3+/SPP1+ WSB1 2.93 4.18E−11 3.21E−11 1.54E−08 NGN3+/SPP1+ PTN 2.89 1.05E−10 0.009197842 7.31E−13 NGN3+/SPP1+ ACOT1 2.89 3.67E−10 7.98E−09 3.49E−09 NGN3+/SPP1+ MIA 2.89 1.34E−08 2.08E−05 4.02E−06 NGN3+/SPP1+ SPHK1 2.85 1.71E−09 8.93E−22 1.90E−09 NGN3+/SPP1+ GSTA3 2.77 4.93E−14 1.16E−37 8.08E−16 NGN3+/SPP1+ EIF1A 2.73 5.61E−06 1 1 NGN3+/SPP1+ MPZL1 2.73 1.18E−11 8.31E−05 2.47E−10 NGN3+/SPP1+ GSTM1 2.69 0.000117653 0.099474698 0.059317455 NGN3+/SPP1+ SMCO4 2.68 1.75E−08 1.52E−11 3.19E−09 NGN3+/SPP1+ IFITM2 2.68 1.45E−12 6.51E−12 4.15E−12 NGN3+/SPP1+ ADK 2.64 5.41E−06 4.61E−06 0.000209261 NGN3+/SPP1+ PEBP1 2.64 7.73E−22 2.86E−13 6.08E−22 NGN3+/SPP1+ GM10260 2.62 3.53E−10 2.77E−07 3.79E−12 NGN3+/SPP1+ GM8773 2.60 0.002145093 6.04E−06 0.155022311 NGN3+/SPP1+ ASS1 2.57 1.58E−05 1.33E−07 2.04E−06 NGN3+/SPP1+ IDH2 2.57 9.00E−14 1.41E−11 2.15E−14 NGN3+/SPP1+ TEAD2 2.57 1.00E−06 3.17E−11 5.23E−07 NGN3+/SPP1+ QSOX1 2.57 3.50E−07 1.16E−07 7.49E−06 NGN3+/SPP1+ ANXA2 2.51 2.09E−06 2.32E−12 1.44E−07 NGN3+/SPP1+ CBFA2T3 2.51 0.000158643 1.23E−06 0.000331285 NGN3+/SPP1+ RPL35 2.50 8.22E−09 1.40E−06 8.65E−11 NGN3+/SPP1+ COL18A1 2.46 2.09E−06 5.05E−15 1.85E−06 NGN3+/SPP1+ MEST 2.45 0.002155698 0.001897746 0.000190086 NGN3+/SPP1+ ENO1 2.45 1.60E−09 9.36E−05 4.39E−09 NGN3+/SPP1+ SPINK4 2.45 0.003319279 2.11E−07 0.001074251 NGN3+/SPP1+ IGF2 2.39 9.01E−05 4.02E−06 9.88E−05 NGN3+/SPP1+ GALK1 2.36 8.62E−06 7.37E−18 2.06E−06 NGN3+/SPP1+ WFDC15B 2.35 2.76E−05 7.02E−12 7.72E−06 NGN3+/SPP1+ BCL2 2.35 3.08E−08 7.33E−19 1.09E−07 NGN3+/SPP1+ ADCK5 2.35 0.005234971 0.002236532 0.148023088 NGN3+/SPP1+ COL9A3 2.33 0.000353329 1.62E−06 0.001544085 NGN3+/SPP1+ LITAF 2.31 0.00116332 1.47E−06 0.001570313 NGN3+/SPP1+ KRT18 2.30 7.16E−07 7.27E−05 1.74E−06 NGN3+/SPP1+ DDB1 2.30 0.001138057 1 1 NGN3+/SPP1+ EPCAM 2.28 2.18E−08 2.49E−05 2.46E−08 NGN3+/SPP1+ ACAA2 2.28 4.85E−05 3.23E−10 9.73E−07 NGN3+/SPP1+ GRIN3A 2.25 4.49E−13 8.16E−42 7.41E−14 NGN3+/SPP1+ AKT1 2.25 0.000168468 1 1 NGN3+/SPP1+ PRDX4 2.25 6.13E−06 0.326316303 1.08E−05 NGN3+/SPP1+ APOE 2.23 0.00680732 0.000167881 0.00542927 NGN3+/SPP1+ PPP1R7 2.22 7.72E−05 1 1 NGN3+/SPP1+ GPC3 2.20 0.014643133 6.60E−06 0.001450543 NGN3+/SPP1+ GADD45G 2.19 0.01234775 0.001320417 0.024717193 NGN3+/SPP1+ FUCA1 2.19 0.004378699 0.185649299 0.019969619 NGN3+/SPP1+ LINGO1 2.16 0.015855507 0.01126812 0.455678237 NGN3+/SPP1+ CASP6 2.14 0.006806863 0.001305367 0.017983122 NGN3+/SPP1+ DLL1 2.14 0.000498752 1.71E−09 0.000492282 NGN3+/SPP1+ CD82 2.13 0.006741105 0.004020626 0.083878393 NGN3+/SPP1+ RPL36A 2.13 4.72E−10 1.02E−08 4.09E−10 NGN3+/SPP1+ HEBP1 2.11 0.000514995 0.040451013 0.232891767 NGN3+/SPP1+ NPTX2 2.11 0.600691538 0.075334124 1 NGN3+/SPP1+ PRPF4B 2.11 0.00016325 1 1 NGN3+/SPP1+ LAMB1 2.11 0.006103244 0.002218251 0.053334127 NGN3+/SPP1+ TUBA4A 2.10 0.000732535 1 1 NGN3+/SPP1+ HMGB3 2.10 0.020006079 0.000938831 0.001565509 NGN3+/SPP1+ UBA52 2.08 0.00272053 0.000103442 0.000396055 NGN3+/SPP1+ DONSON 2.08 0.000265499 0.031969214 0.101836399 NGN3+/SPP1+ VIM 2.07 1.59E−05 5.39E−08 4.24E−05 NGN3+/SPP1+ RCN2 2.07 3.16E−05 1.33E−05 3.31E−05 NGN3+/SPP1+ CRP 2.07 2.97E−06 2.42E−16 0.000127285 NGN3+/SPP1+ SNRPG 2.06 1.20E−05 0.000221458 3.12E−05 NGN3+/SPP1+ BAMBI 2.06 1 1 1 NGN3+/SPP1+ PRSS23 2.04 0.000525454 1.59E−10 0.000338145 NGN3+/SPP1+ APOC1 2.04 0.608170266 0.000723679 0.456146098 NGN3+/SPP1+ FOXA3 2.03 1.46E−05 1.64E−07 1.28E−05 NGN3+/SPP1+ CLDN12 2.03 0.009399822 5.42E−08 0.010828941 NGN3+/SPP1+ ATOX1 2.01 0.0008086155 0.00427101 0.000245496 NGN3+/SPP1+ NEUROG3 6.45 6.29E−82 1.91E−59 1.65E−64 NGN3+ BTBD17 6.32 2.20E−70 7.08E−56 6.87E−66 NGN3+ TGM7 5.98 3.66E−18 9.32E−27 7.34E−17 NGN3+ GADD45A 5.90 4.18E−66 2.15E−53 6.65E−62 NGN3+ TMSB4X 4.59 1.10E−62 1.66E−43 2.27E−60 NGN3+ MDK 4.32 2.60E−74 1.86E−46 8.43E−61 NGN3+ NEUROD2 3.68 2.53E−33 7.24E−44 1.40E−32 NGN3+ CCK 3.51 2.16E−19 5.81E−15 2.55E−18 NGN3+ SOX4 3.43 5.44E−32 5.19E−31 1.49E−30 NGN3+ MFNG 3.39 2.35E−37 1.60E−37 7.26E−36 NGN3+ IGFBPL1 3.32 1.70E−34 3.27E−32 3.12E−33 NGN3+ HES6 3.29 1.19E−42 5.82E−34 3.86E−41 NGN3+ SMARCD2 3.27 3.03E−32 6.15E−30 3.84E−31 NGN3+ SNRK 3.23 8.40E−18 1.27E−18 2.12E−15 NGN3+ CLDN6 3.20 8.49E−37 6.07E−31 2.43E−35 NGN3+ KIRREL2 3.18 2.18E−24 4.06E−31 5.50E−22 NGN3+ GRASP 3.14 3.07E−23 4.03E−25 2.41E−21 NGN3+ IFITM2 2.93 2.36E−31 1.62E−28 5.49E−30 NGN3+ TECR 2.87 2.52E−44 1.19E−28 1.14E−42 NGN3+ PPP1R14A 2.87 3.65E−24 1.30E−34 3.58E−23 NGN3+ TTLL6 2.83 4.24E−08 1.28E−09 3.83E−10 NGN3+ DDIT4 2.81 1.78E−21 2.45E−29 7.47E−22 NGN3+ SELM 2.81 6.73E−31 8.07E−26 2.96E−29 NGN3+ CDC14B 2.77 2.05E−18 4.00E−19 1.60E−13 NGN3+ PLK3 2.75 4.72E−21 4.71E−30 6.35E−21 NGN3+ PPIB 2.73 3.81E−41 9.77E−34 2.30E−39 NGN3+ AMOTL2 2.71 9.84E−18 9.10E−25 8.12E−18 NGN3+ GPX2 2.68 2.69E−17 1.19E−19 1.24E−17 NGN3+ CDK2AP1 2.64 2.37E−25 2.20E−25 4.84E−25 NGN3+ TEAD2 2.62 4.79E−19 1.56E−25 3.52E−20 NGN3+ HN1 2.58 2.52E−31 1.14E−26 2.36E−30 NGN3+ EPB42 2.53 1.08E−17 2.84E−27 3.58E−17 NGN3+ PAX4 2.53 1.76E−10 7.44E−12 1.47E−10 NGN3+ FOXA3 2.53 1.28E−15 1.56E−16 2.11E−14 NGN3+ TUBB3 2.51 9.85E−19 2.41E−16 6.41E−18 NGN3+ CER1 2.50 1.01E−12 2.48E−18 1.33E−12 NGN3+ MFAP4 2.43 9.52E−08 1.80E−09 7.47E−08 NGN3+ MTCH1 2.41 1.07E−30 5.90E−26 2.61E−29 NGN3+ CDK4 2.39 1.90E−32 2.94E−27 1.01E−30 NGN3+ COTL1 2.38 3.94E−21 4.22E−15 1.46E−20 NGN3+ SPARC 2.36 1.55E−08 3.56E−10 1.21E−08 NGN3+ TMEM184A 2.33 1.10E−07 9.81E−08 2.75E−06 NGN3+ 2010107G23RIK 2.31 3.94E−13 1.31E−10 1.26E−13 NGN3+ LY6E 2.28 1.20E−18 8.26E−18 2.10E−19 NGN3+ TPM4 2.28 8.49E−18 7.46E−20 1.59E−18 NGN3+ KRTAP17-1 2.27 7.54E−08 4.54E−12 9.55E−09 NGN3+ MARCKSL1 2.27 7.04E−29 1.06E−24 2.68E−25 NGN3+ GM42637 2.27 9.05E−12 3.44E−18 5.48E−12 NGN3+ OLFM1 2.27 7.65E−10 9.90E−11 7.52E−10 NGN3+ UPK3BL 2.23 2.37E−11 4.75E−18 3.41E−12 NGN3+ KRT19 2.23 6.82E−06 9.60E−07 2.70E−07 NGN3+ CLPS 2.22 1.18E−05 2.68E−07 3.06E−06 NGN3+ TMEM171 2.20 4.80E−08 3.57E−12 9.46E−08 NGN3+ RCOR2 2.20 1.97E−08 3.04E−08 1.95E−07 NGN3+ GFRA3 2.20 1.01E−08 2.95E−05 1.28E−07 NGN3+ SERPINH1 2.17 2.10E−15 7.85E−16 9.10E−15 NGN3+ SULT2B1 2.14 1.24E−16 6.27E−25 4.94E−17 NGN3+ LPAR6 2.13 3.03E−11 5.54E−14 6.75E−11 NGN3+ RRM1 2.13 5.05E−05 4.21E−06 0.000216461 NGN3+ TTC28 2.11 2.64E−11 3.03E−15 1.95E−12 NGN3+ CASP6 2.11 6.19E−14 1.35E−16 1.17E−13 NGN3+ SULF2 2.10 2.31E−10 1.94E−14 3.18E−10 NGN3+ ADAM10 2.08 3.83E−05 0.019849605 0.590924318 NGN3+ FYTTD1 2.07 3.42E−13 7.13E−15 8.61E−13 NGN3+ CBFA2T3 2.06 3.67E−08 4.57E−12 8.84E−09 NGN3+ TPST2 2.06 6.48E−10 6.12E−09 8.10E−09 NGN3+ GDPD1 2.03 2.25E−10 4.01E−13 2.54E−10 NGN3+ INSM1 2.01 5.22E−08 2.98E−09 5.22E−07 NGN3+ FEV 4.53 4.34E−25 1.33E−22 8.77E−24 FEV+/PAX4+ RUNX1T1 3.20 1.97E−22 3.62E−21 1.23E−18 FEV+/PAX4+ CACNA2D1 3.12 3.05E−20 4.55E−19 1.54E−18 FEV+/PAX4+ 1110012L19RIK 3.10 1.24E−17 1.28E−12 9.10E−16 FEV+/PAX4+ CLDN4 2.68 3.55E−17 1.66E−13 7.63E−14 FEV+/PAX4+ GSPT1 2.66 1.77E−12 7.47E−11 5.83E−12 FEV+/PAX4+ PAX4 2.51 2.46E−16 4.79E−20 4.04E−16 FEV+/PAX4+ KRT7 2.46 1.14E−20 7.08E−18 2.53E−18 FEV+/PAX4+ BC023829 2.39 2.32E−12 5.98E−13 1.14E−11 FEV+/PAX4+ TOX3 2.27 1.89E−15 1.77E−14 7.26E−14 FEV+/PAX4+ POU3F4 2.23 3.87E−05 5.78E−06 1.86E−05 FEV+/PAX4+ CHGB 2.22 0.000955337 0.000784048 0.000366104 FEV+/PAX4+ KRT8 2.20 9.09E−19 3.17E−17 1.46E−16 FEV+/PAX4+ ELAVL4 2.16 0.000162372 1.31E−05 0.000849094 FEV+/PAX4+ JUN 2.14 3.56E−09 7.77E−10 3.56E−08 FEV+/PAX4+ BTG2 2.07 0.000198515 0.037190083 0.005256782 FEV+/PAX4+ RASD1 2.06 0.01243511 0.012293278 0.027450346 FEV+/PAX4+ LHX1OS 2.04 1.41E−05 2.83E−09 3.04E−05 FEV+/PAX4+ VWA5B2 2.04 3.00E−07 1.10E−05 8.83E−05 FEV+/PAX4+ LHX1 2.00 0.00231933 9.95E−06 0.005706814 FEV+/PAX4+ CHGB 3.71 1.58E−40 1.11E−32 4.93E−36 FEV+/CHGB+ VIM 3.68 2.24E−23 1.47E−23 2.45E−21 FEV+/CHGB+ KLF2 2.95 5.48E−15 8.42E−16 5.39E−14 FEV+/CHGB+ CRYBA2 2.87 6.88E−21 1.20E−19 1.52E−20 FEV+/CHGB+ HBB-BT 2.83 0.876379076 1 1 FEV+/CHGB+ CLDN4 2.64 2.15E−22 3.29E−22 3.64E−24 FEV+/CHGB+ GCH1 2.57 7.92E−22 5.05E−22 6.19E−23 FEV+/CHGB+ USP18 2.36 3.56E−13 6.47E−17 5.20E−14 FEV+/CHGB+ FEV 2.36 6.29E−30 1.31E−27 2.12E−29 FEV+/CHGB+ HMGN3 2.20 3.54E−26 8.10E−23 2.36E−25 FEV+/CHGB+ CHGA 2.20 2.64E−14 3.32E−14 7.41E−15 FEV+/CHGB+ FOS 2.16 4.24E−17 1.96E−17 2.43E−17 FEV+/CHGB+ RAP1B 2.07 1.62E−20 3.08E−19 7.70E−20 FEV+/CHGB+ JUNB 2.07 1.16E−07 2.97E−07 3.59E−07 FEV+/CHGB+ INS2 230.72 1.01E−88 2.70E−57 9.05E−68 BETA INS1 59.71 1.47E−54 9.45E−65 5.91E−48 BETA NNAT 20.53 7.60E−82 5.16E−55 4.19E−66 BETA NPY 9.13 4.98E−25 4.90E−37 2.19E−19 BETA PPP1R1A 8.00 3.76E−65 2.97E−66 5.77E−56 BETA IAPP 7.21 2.60E−47 5.86E−44 1.14E−38 BETA SDF2L1 5.06 1.76E−42 1.40E−34 1.74E−33 BETA CRELD2 3.86 5.37E−41 5.57E−23 7.14E−30 BETA GNG12 3.78 4.68E−39 2.55E−36 3.37E−37 BETA MANF 3.66 2.87E−45 1.30E−32 9.48E−39 BETA ATP2A2 3.51 3.51E−43 4.35E−35 3.34E−42 BETA HADH 2.93 1.80E−25 1.20E−25 2.71E−22 BETA SLC2A2 2.91 1.31E−32 1.03E−47 3.59E−26 BETA OCIAD2 2.89 2.94E−24 1.71E−29 1.48E−19 BETA G6PC2 2.71 1.24E−21 1.33E−30 5.75E−16 BETA PDIA6 2.68 1.37E−28 1.35E−23 2.44E−26 BETA DLK1 2.57 1.70E−26 2.41E−24 2.03E−25 BETA CALR 2.55 7.67E−26 1.76E−20 3.53E−23 BETA SYTL4 2.53 9.16E−27 1.59E−38 2.50E−21 BETA DNAJB11 2.38 2.18E−23 1.45E−18 9.83E−25 BETA SCG2 2.36 2.40E−16 5.89E−23 3.21E−12 BETA PCSK2 2.35 3.77E−26 2.26E−23 9.95E−21 BETA PDX1 2.30 3.96E−24 1.37E−23 2.34E−22 BETA SURF4 2.27 3.03E−17 3.42E−14 2.34E−14 BETA ERO1LB 2.22 1.08E−19 1.22E−26 4.61E−16 BETA OSTC 2.20 9.86E−25 9.95E−22 1.44E−23 BETA HSPA5 2.19 1.95E−25 1.60E−23 7.72E−26 BETA SERP1 2.19 2.43E−19 6.69E−18 4.42E−19 BETA CDK2AP2 2.16 3.05E−17 1.08E−12 4.30E−16 BETA SEC61B 2.13 4.18E−30 2.69E−25 2.95E−29 BETA MAFB 2.08 1.23E−12 5.03E−15 5.78E−09 BETA PAPSS2 2.07 1.53E−15 4.36E−21 6.96E−11 BETA 1700086L19RIK 2.07 7.90E−10 1.74E−09 4.17E−08 BETA DBPHT2 2.06 1.23E−08 7.93E−08 1.04E−05 BETA HSP90B1 2.04 5.21E−14 2.14E−12 4.68E−13 BETA TUBB4B 2.01 6.50E−10 3.00E−09 1.50E−05 BETA TUBB2A 2.01 2.10E−10 3.72E−12 2.47E−07 BETA FAM151A 2.01 1.33E−11 9.18E−17 1.16E−08 BETA GCG 111.43 8.65E−68 4.25E−55 5.03E−67 ALPHA GAST 14.93 3.06E−33 4.83E−41 2.03E−32 ALPHA TMEM27 7.94 2.79E−72 7.46E−64 6.37E−70 ALPHA PEG10 6.11 1.92E−51 3.06E−56 1.59E−51 ALPHA PPY 5.86 1.47E−10 7.03E−12 3.75E−10 ALPHA PYY 5.82 1.21E−53 7.76E−43 3.57E−47 ALPHA TTR 4.92 1.97E−46 4.05E−37 4.70E−44 ALPHA ZCCHC18 3.81 2.22E−35 5.46E−33 1.23E−33 ALPHA SLC38A5 3.29 2.96E−36 4.50E−32 6.56E−33 ALPHA IRX1 3.20 1.12E−34 1.50E−47 2.42E−34 ALPHA IRX2 3.14 2.83E−30 1.81E−40 2.46E−30 ALPHA TMSB15B2 2.93 4.66E−14 2.17E−17 6.47E−13 ALPHA GPX3 2.73 6.78E−25 1.52E−23 1.30E−24 ALPHA WNK3 2.57 6.25E−21 2.33E−28 3.52E−20 ALPHA PCSK1N 2.53 4.37E−26 1.78E−24 4.29E−25 ALPHA PAM 2.48 5.05E−23 9.54E−23 3.11E−23 ALPHA RNF130 2.43 6.04E−23 5.79E−17 2.42E−22 ALPHA PON3 2.39 6.38E−21 4.64E−28 2.15E−19 ALPHA RESP18 2.36 2.35E−12 5.12E−17 6.05E−12 ALPHA SMARCA1 2.36 3.13E−13 7.36E−17 2.87E−13 ALPHA CTXN2 2.30 1.51E−17 9.92E−27 8.62E−18 ALPHA MEIS2 2.30 1.24E−16 5.33E−16 1.71E−15 ALPHA SCT 2.25 4.17E−10 8.32E−11 1.61E−09 ALPHA USH1C 2.25 2.64E−15 3.68E−19 4.76E−14 ALPHA RBP4 2.23 2.36E−20 1.49E−18 2.27E−18 ALPHA CTSZ 2.23 8.43E−15 4.41E−11 2.64E−14 ALPHA TMED3 2.19 8.02E−18 1.57E−19 5.52E−17 ALPHA SCG5 2.16 5.79E−12 1.09E−13 1.98E−11 ALPHA CPE 2.16 3.11E−27 8.43E−24 8.65E−24 ALPHA ARX 2.13 2.39E−11 3.80E−14 7.98E−12 ALPHA SLC16A10 2.11 3.41E−15 1.76E−21 5.17E−15 ALPHA PCSK2 2.10 1.39E−14 1.06E−14 7.09E−14 ALPHA GHRL 32.45 1.68E−19 2.63E−36 6.82E−21 EPSILON CDKN1A 9.65 3.01E−31 3.54E−22 2.14E−30 EPSILON MBOAT4 6.68 3.87E−40 6.50E−67 2.71E−39 EPSILON HHEX 4.82 6.38E−07 1.70E−09 1.32E−06 EPSILON RBP4 4.47 1.89E−06 0.000434406 2.56E−07 EPSILON ISL1 4.38 4.18E−23 5.98E−20 1.02E−23 EPSILON MAGED2 3.94 2.30E−23 0.164097154 3.80E−12 EPSILON ARG1 3.73 6.94E−15 5.19E−23 6.66E−15 EPSILON PYY 2.99 0.09508771 0.002081274 0.007516006 EPSILON ANPEP 2.77 0.043169744 3.60E−06 0.009815685 EPSILON RGS17 2.77 1.09E−10 3.73E−11 3.49E−10 EPSILON FHL2 2.41 0.000104472 0.000523591 4.64E−05 EPSILON NEFM 2.39 0.00200021 1.65E−09 0.00035343 EPSILON B630019K06RIK 2.33 0.00017761 0.00661927 0.001346362 EPSILON CTSL 2.31 0.000695583 0.123653648 0.030180958 EPSILON TSPAN12 2.27 0.001318175 1.11E−06 0.004086006 EPSILON PEG3 2.22 7.46E−07 0.000194082 2.84E−06 EPSILON SYNE1 2.22 2.95E−08 9.15E−14 8.18E−07 EPSILON FFAR4 2.20 5.53E−05 2.92E−06 2.09E−05 EPSILON CD200 2.13 0.006569163 3.25E−05 0.001500125 EPSILON GHR 2.11 0.000557467 1.99E−05 0.000138432 EPSILON CD24A 2.08 0.010616908 0.001186314 0.006341855 EPSILON GJD2 2.07 0.005998637 0.003444585 0.16872243 EPSILON NAP1L5 2.04 0.583506634 0.031631734 0.895645277 EPSILON DHRS7 2.04 0.002440467 0.088577295 0.024914348 EPSILON ATF3 2.00 1 0.070900166 1 EPSILON
TABLE-US-00003 TABLE 2 MEMBERS.sub.— - LOG MEMBERS.sub.— INPUT.sub.— EFFEC- (P- INPUT.sub.— OVERLAP.sub.— TIVE.sub.— P-VALUE Q-VALUE VALUE) PATHWAY SOURCE EXTERNAL_ID OVERLAP GENEIDS SIZE SIZE 0.00015116 0.00708594 3.82055691 GLUTATHIONE HUMANCY C PWY-4081 GPX3; 2878; 2882 9 9 REDOX GPX7 REACTIONS I 0.00023034 0.00708594 3.6376214 BMP2-WNT4-FOXO1 WIKIPATH- WP3876 DCN; 1634; 6422 11 11 PATHWAY IN WAYS SFRP1 HUMAN PRIMARY ENDOMETRIAL STROMAL CELL DIFFERENTIATION 0.00027606 0.00708594 3.5590024 REACTIVE HUMANCY C DETOX1- GPX3; 2878; 2882 12 12 OXYGEN SPECIES PWY-1 GPX7 DEGRADATION 0.00032583 0.00708594 3.48701392 SENESCENCE- WIKIPATH- WP3391 IGFBP3; 3490; 3486 13 13 ASSOCIATED WAYS IGFBP7 SECRETORY PHENOTYPE (SASP) 0.00043748 0.00708594 3.35904343 NEGATIVE REACTOME R-HSA- SFRP2; 6422; 6423 15 15 REGULATION OF SFRP1 TCF-DEPENDENT SIGNALING BY WNT LIGAND ANTAGONISTS 0.00048313 0.00708594 3.31593424 THYROID HORMONE KEGG PATH: GPX3; 2878; 2882; 74 74 SYNTHESIS - HSA04918 GPX7; 481 HOMO SAPIENS ATP1B1 (HUMAN) 0.00056517 0.00710505 3.24781779 ACE INHIBIT WIKIPATH- WP554 ACE2; 59272; 186 17 17 OR PATHWAY WAYS AGTR2 0.00078652 0.00753487 3.1042886 ACE INHIBIT OR PHARMGKB PA2023 ACE2; 59272; 186 20 20 PATHWAY, PHARMA- AGTR2 CODYNAMICS 0.00078652 0.00753487 3.1042886 AGENTS ACTING ON PHARMGKB PA165110622 ACE2; 59272; 186 20 20 THE RENINANGIO- AGTR2 TENSIN SYSTEM PATHWAY, PHARMA- CODYNAMICS 0.00085624 0.00753487 3.06740679 PROTEIN DIGENTION KEGG PATH: ACE2; 7373; 59272; 90 90 AND ABSORPTION - HSA04974 COL14A1; 481 HOMO SAPIENS ATP1B1 (HUMAN) 0.00104326 0.0083461 2.98160665 RENINANGIO- KEGG PATH: ACE2; 59272; 186 23 23 TENSIN SYSTEM - HSA04614 AGTR2 HOMO SAPIENS (HUMAN) 0.00149005 0.01092701 2.82680027 POST-TRANSLA- REACTOME R-HSA- IGFBP3; 11098; 3486; 110 109 TIONAL PROTEIN 8957275 IGFBP7; 3490 PHOSPHORYLATION PRSS23 0.00177759 0.01111899 2.75016744 #NAME? BIOCARTA BARREST AGTR2; 186; 6387 30 30 INPATHWAY CXCL12 0.00189773 0.01111899 2.72176486 ACTIVATION OF BIOCARTA GSPATHWAY AGTR2; 186; 6387 31 31 CAMP-DEPENDENT CXCL12 PROTEIN KINASE PKA 0.00202164 0.01111899 2.6942972 ROLE OF - BIOCARTA BARRMAPK AGTR2; 186; 6387 32 32 ARRESTINS IN THE PATHWAY CXCL12 ACTIVATION AND TARGETING OF MAP KINASES 0.00202164 0.01111899 2.6942972 WNT-NCORE SIGNALINK NONE SFRP2; 6422; 6423 32 32 0.00225618 0.01124011 2.64662575 REGULATION OF REACTOME R-HSA- SFRP1 11098; 3486; 127 126 INSULIN-LIKE 381426 IGFBP3; 3490 GROWTH FACTOR IGFBP7; (IGF) TRANSPORT PRSS23 AND UPTAKE BY INSULIN-LIKE GROWTH FACTOR FACTOR BINDING PROTEINS (IGFBPS) 0.00241577 0.01124011 2.61694474 SMOOTH MUSCLE REACTOME R-HSA- ACTA2; 59; 10398 35 35 CONTRACTION 445355 MYL9 0.00255457 0.01124011 2.59268198 DETOXIFICATION REACTOME R-HSA- GPX3; 2878; 2882 36 36 OF REACTIVE 3299685 GPX7 OXYGEN SPECIES 0.00255457 0.01124011 2.59268198 ROLES OF ARRESTIN BIOCARTA BARREST AGTR2; 6387; 186 36 36 DEPENDENT INSRCPATH- CXCL12 RECRUITMENT WAY OF SRC KINASES GPCR SIGNALING 0.00284323 0.01191447 2.54618867 STRIATED MUSCLE WIKIPATH- WP383 ACTA2; 10398; 59 38 38 CONTRACTION WAYS MYL9 0.00305341 0.01221363 2.51521514 EXTRACELULAR REACTOME R-HSA- DCN; LOX; 1634; 4060; 293 293 MATRIX 1474244 COL14A1; 7373; 4015 ORGANIZATION LUM 0.00346431 0.01325476 2.46038305 CHREBP BIOCARTA CHREBP- AGTR2; 186; 6387 42 42 REGULATION BY PATHWAY CXCL12 CARBOHYDRATES AND CAMP 0.00396797 0.01454921 2.40143204 ACTIVATION OF BIOCARTA CSKPATH- AGTR2; 186, 6387 45 45 CSK BY CAMP- WAY CXCL12 DEPENDENT PROTEIN KINASE INHIBITS SIGNALING THROUGH THE T CELL RECEPTOR 0.00432154 0.0152118 2.36436192 ION CHANNELS BIOCARTA RACCPATH- AGTR2; 186; 6387 47 47 AND THEIR WAY CLCL12 FUNCTIONAL ROLE IN VASCULAR ENDOTHELIUM 0.0048783 0.01651116 2.31173164 CHEMOKINE REACTOME R-HSA- CXCL13; 6387; 10563 50 50 RECEPTORS BIND CXCL12 CHEMOKINES 0.00526691 0.01716623 2.27844398 ONE CARBON WIKIPATH- WP3940 GPX3; 2882; 2878 52 52 METABOLISM AND WAYS GPX7 RELATED PATHWAYS 0.00566934 0.01781793 2.24646741 GLUTATHIONE KEGG PATH: GPX3; 2878; 2882 54 54 METABOLISM - HSA00480 GPX7 HOMO SAPIENS (HUMAN) 0.00629864 0.01911312 2.200753 ECM REACTOME R-HSA- DCN; LUM 4060; 1634 57 57 PROTEOGLYCANS 3000178 0.00692947 0.02032643 2.15930023 TCF DEPENDENT REACTOME R-HSA- RSPO3; 6422; 84870; 190 188 SIGNALING IN 201681 SFRP2; 6423 RESPONSE TO WNT SFRP1 0.00741496 0.02043259 2.12989121 ARACHIDONIC KEGG PATH: GPX3; 2878; 2882 62 62 ACID METABOLISM - HSA00590 GPX7 HOMO SAPIENS (HUMAN) 0.00755484 0.02043259 2.12177496 PEPTIDE REACTOME R-HSA- AGTR2; 10563; 6387; 194 194 LIGAND-BINDING 375276 CXCL13; 186 RECEPTORS CXCL12 0.00766222 0.02043259 2.11564526 MUSCLE REACTOME R-HSA- ACTA2; 59; 10398; 195 195 CONTRACTION 397014 ATP1B1; 481 MYL9 0.00899427 0.02327928 2.04603416 G ALPHA (I) REACTOME R-HSA- AGTR2; 186; 10563; 399 398 SIGNALLING EVENTS 418594 CXCL13; 5947; 6387 CXCL12; RBP1 0.00989439 0.02487732 2.00461101 LINOLEATE EHMN LENOLEATE GPX3; 2878; 2882 74 72 METABOLISM METABOLISM GPX7
TABLE-US-00004 TABLE 3 MEM- MEMBERS.sub.— BERS.sub.— INPUT.sub.— EFFEC- - LOG EXTER- INPUT.sub.— OVERLAP.sub.— TIVE.sub.— P-VALUE Q-VALUE (P-VALUE) PATHWAY SOURCE NAL_ID OVERLAP GENEIDS SIZE SIZE 9.37E−06 0.00231525 5.028099335 DIRECT PID P53DOWN- CASP6; 1647; 839; 147 146 P53 STREAM- DDIT4; 80781; 6696; EFFECTORS PATHWAY COL18A1; 54541; 1263; GADD45A; 1026 PLK3; SPP1; CDKN1A 3.57E−05 0.00293572 4.447861411 P53 BIOCARTA P53 GADD45A; 1647; 1019; 13 13 SIGNALING PATHWAY CDKN1A; 1026 PATHWAY CDK4 3.57E−05 0.00293572 4.447861411 TP53 WIKI- WP3804 GADD45A; 1647; 1026; 13 13 REGULATES PATHWAYS CDKN1A; 1263 13 13 TRANSCRIPTION PLK3 OF CELL CYCLE GENES 7.37E−05 0.00455357 4.132285259 RETINOID REACTOME R-HSA- RBP1; 5947; 348; 44 44 METABOLISM AND 975634 CLPS; 1208; 2719 TRANSPORT APOE; GPC3 0.0001426 0.00704462 3.845869661 METABOLISM OF REACTOME R-HSA- RPB1; 5947; 348; 52 52 FAT-SOLUBLE 6806667 CLPS; 1208; 2719 VITAMINS APOE; GPC3 0.0002572 0.01058812 3.589727046 MIR-517 RELA- WIKI- WP3596 CDKN1A; 1026; 3397 5 5 TIONSHIP WITH PATHWAYS ID1 ARCN1 AND USP1 0.00045069 0.01590303 3.346119072 COLLAGEN REACTOME R-HSA- COL18A1; 80781; 5479; 70 70 BIOSYNTHESIS 1650814 SERPINH1; 871; 1299 AND MODIFYING PPIB; ENSYMES COL9A3 0.00053655 0.01656589 3.270392095 ETHANOL SMPDB SMP00449 ALDH1B1; 847; 219 7 7 DEGRADATION CAT 0.00074755 0.0202084 3.126357882 EXTRACELLULAR REACTOME R-HSA- COL18A1; 5479; 871; 295 294 MATRIX 1474244 COL9A3; 1299; 6678; ORGANIZATION ADAM10; 6696; 102; SPARC; 80781 SPP1; SERPINH1; PPIB 0.00081815 0.0202084 3.087164937 COLLAGEN REACTOME R-HSA- COL18A1; 80781; 102; 36 36 DEGRADATION 1442490 ADAM10; 1299 COL9A3 0.00128691 0.02634768 2.890451927 BINDING AND REACTOME R-HSA- SPARC; 348; 259; 42 42 UPTAKE OF 2173782 APOE; 6678 LIGANDS BY AMBP SCAVENGER RECEPTORS 0.00136719 0.02634768 2.864172626 COLLAGEN REACTOME R-HSA- COL18A1; 871; 5479; 94 94 FORMATION 1474290 SERPINH1; 1299; 80781 PPIB; COL9A3 0.00138672 0.02634768 2.858011158 REACTIVE HUMANCY DETOX1- GPX2; 847; 2877 12 11 OXYGEN PWY-1 CAT SPECIES DEGRADATION 0.00184777 0.02936699 2.733352899 VISUAL PHOTO- REACTOME R-HSA- RBP1; 5947; 348; 102 102 TRANSDUCTION 2187338 CLPS; 2719; 1208 APOE; GPC3 0.00191508 0.02936699 2.717812136 CELL CYCLE WIKI- WP179 GADD45A; 1026; 1019; 103 103 PATHWAYS CDKN1A; 1647; 8555 CDK4; CDC14B 0.00195363 0.02936699 2.70915735 RB TUMOR BIOCARTA RBPATH- CDKN1A; 1019; 1026 13 13 SUPPRESSOR/ WAY CDK4 CHECKPOINT SIGNALING IN RESPONSE TO DNA DAMAGE 0.0022014 0.02936699 2.657301775 DEGRADATION REACTOME R-HSA- COL18A1; 1299; 80781; 107 107 OF THE EXTRA- 1474228 ADAM10; 6696; 102 CELLULAR MATRIX SPP1; COL9A3 0.002259 0.02936699 2.646083889 TP53 REGULATES REACTOME R-HSA- GADD45A; 1263; 1647; 51 51 TRANSCRIPTION 6791312 PLK3; 1026 OF CELL CYCLE CDKN1A GENES 0.002259 0.02936699 2.646083889 NOTCH-MEDIATED PID HES_HEY- NEUROG3; 3397; 55502; 51 51 HES/HEY NETWORK PATHWAY HES6; 50674 ID1 0.00252267 0.03115497 2.598139565 BILE ACID EHMN BILE ACID DBI; 641371; 53 53 BIOSYNTHESIS BIOSYNTHE- ALDH1B1; 1622; 219 SIS ACOT1 0.00266134 0.03130241 2.574899912 FATTY ACYLCOA REACTOME R-HSA- DBI; TECR; 641371; 55 54 BIOSYNTHESIS 75105 ACOT1 1622; 9524 0.00310529 0.03441547 2.507897216 VALIDATED PID TAP63- GADD45A; 1647; 1026; 57 57 TRANSCRIOTIONAL PATHWAY CDKN1A; 2877 TARGETS OF GPX2 TAP63 ISOFORMS 0.00375024 0.03441547 2.425940769 CELL CYCLE - KEGG PATH: GADD45A; 1026; 1647; 124 124 HOMO SAPIENS HSA04110 CDKN1A; 8555; 1019 (HUMAN) CDK4; CDC14B 0.00376924 0.03441547 2.423746639 PROPANOATE EHMN PROPANO- ALDH1B1; 219; 1622 18 18 METABOLISM ATE DBI METABO- LISM 0.00376924 0.03441547 2.423746639 SCF(SKP2)- REACTOME R-HSA- CDKN1A; 1019; 1026 18 18 MEDIATED 187577 CDK4 DEGRADATION OF P27/P21 0.00376924 0.03441547 2.423746639 REGULATION OF BIOCARTA PLK3- CASP6; 1263; 839 18 18 CELL CYCLE PRO- PATHWAY PLK3 GRESSION BY PLK3 0.00419877 0.03441547 2.37687761 TP53 WIKI- WP1742 GADD45A; 1647; 1026 19 19 NETWORK PATHWAYS CDKN1A 0.00419877 0.03441547 2.37687761 ATM SIGNALING WIKI- WP2516 GADD45A; 1647; 1026 19 19 PATHWAY PATHWAYS CDKN1A 0.00469983 0.03441547 2.327918116 INTEGRATED WIKI- WP1984 RASGRP3; 1263; 25780; 66 66 BREAST CANCER PATHWAYS PLK3; 1647 PATHWAY 0.00481196 0.03441547 2.31767816 FOXOSIGNALING KEGG PATH: GADD45A; 1026; 1647; 134 133 PATHWAY - HSA04068 CDKN1A; 847; 1263 HOMO SAPIENS PLK3; CAT (HUMAN) 0.00481196 0.03441547 2.31767816 PLATELET REACTOME R-HSA- TMSB4X; 7114; 5768; 133 133 DEGRANULATION 114608 CLU; 6678; 1191 SPARC; QSOX1 0.00490227 0.03441547 2.309603092 INTEGRIN CELL REACTOME R-HSA- COL18A1; 80781; 6696; 68 67 SURFACE 216083 SPP1; 1299 INTERACTIONS COL9A3 0.00510991 0.03441547 2.29158702 G1 TO S CELL WIKI- WP45 GADD45A; 1019; 1026; 68 68 CYCLE CONTROL PATHWAYS CDKN1A; 1647 CDK4 0.00510991 0.03441547 2.29158702 DNA DAMAGE WIKI- WP707 GADD45A; 1019; 1647; 68 68 RESPONSE PATHWAYS CDKN1A; 1026 CDK4 0.00512243 0.03441547 2.29052415 NOTCH2 REACTOME R-HSA- ADAM10; 4192; 102 21 21 ACTIVATION AND 2979096 MDK TRANSMISSION OF SIGNAL TO THE NUCLEUS 0.00532279 0.03441547 2.273861029 P53 SIGNALING KEGG PATH: GADD45A; 1026; 1019; 69 69 PATHWAY - HOMO HSA04115 CDKN1A; 1647 SAPIENS (HUMAN) CDK4 0.00548126 0.03441547 2.26111991 RESPONSE TO REACTOME R-HSA- TMSB4X; 7114; 5768; 138 138 ELEVATED PLATELET 76005 CLU; 6678; 1191 CYTOSOLIC CA2+ SPARC; QSOX1 0.00554094 0.03441547 2.25641663 BETA1 INTEGRIN PID INTEGRIN1.sub.— COL18A1; 6696; 80781; 70 70 CELL SURFACE PATHWAY MDK; 4192 INTERACTIONS SPP1 0.0056161 0.03441547 2.250565538 CELL CYCLE: BIOCARTA G2PATH- GADD45A; 1647; 1026 22 22 G2/M CHECKPOINT WAY CDKN1A 0.0056161 0.03441547 2.250565538 TRYPTOPHAN EHMN TRYPTO- ALDH1B1; 4257; 219 22 22 METABOLISM PHAN META- MGST1 BOLISM 0.00593092 0.03441547 2.226877976 SIGNAL REACTOME R-HSA- RASGRP3; 84894; 9350; 2538 2524 TRANSDUCTION 162582 SPHK1; 4192; 25780; AMOTL2; 1026; 6696; MFNG; 25805; 885; CDKN1A; 102; 8877; ADAM10; 5947; 8555; CCK; 1208; 1019; CER1; 1299; 51421; SOX4; 4242; 92474; MDK; 2676; 6659; APOE; 348; 2719 LINGO1; CDK4; CLPS; PPP1R14A; COL9A3; RBP1; BAMBI; GPC3; CDC14B; SPP1; GFRA3 0.00613069 0.03441547 2.212490542 CYCLINS AND CELL BIOCARTA CELL- CDKN1A; 1019; 1026 23 23 CYCLE REGULATION CYCLE- CDK4 PATHWAY 0.00613069 0.03441547 2.212490542 HYPOXIA AND P53 BIOCARTA P53- GADD45A; 1647; 1026 23 23 IN THE CARDIO- HYPOXIA- CDKN1A VASCULAR SYSTEM PATHWAY 0.00613069 0.03441547 2.212490542 BIOSYNTHESIS OF KEGG PATH: TECR; 9524; 641371 23 23 UNSATURATED FATTY HSA01040 ACOT1 ACIDS - HOMO SAPIENS (HUMAN) 0.006666 0.0349761 2.176134999 BUTANOATE EHMN BUTANOATE ALDH1B1; 219; 1622 24 24 METABOLISM METABO- DBI LISM 0.00671203 0.03497611 2.173146207 VALIDATED PID MYC_RE- GADD45A; 1647; 1026; 75 75 TARGETS OF C-MYC PRESS- CDKN1A; 1191 TRANSCRIPTIONAL PATHWAY CLU REPRESSION 0.00722179 0.03497611 2.141355316 DISULFIRAM SMPDB SMP00429 ALDH1B1; 219; 847 25 25 ACTION PATHWAY CAT 0.00722179 0.03497611 2.141355316 STATIN PATHWAY, PHARMGKB PA2031 APOE; 341; 348 25 25 PHARMACO- APOC1 DYNAMICS 0.00722179 0.03497611 2.141355316 CELL CYCLE: BIOCARTA G1PATH- CDKN1A; 1026; 1019 25 25 G1/S CHECKPOINT WAY CDK4 0.00722179 0.03497611 2.141355316 PROPANOATE INOH NONE ALDH1B1; 3939; 219 25 25 METABOLISM LDHA 0.00722179 0.03497611 2.141355316 FATTY ACID KEGG PATH: TECR; 9524; 641371 25 25 ELONGATION - HOMO HSA00062 ACOT1 SAPIENS (HUMAN) 0.00779785 0.03566794 2.108025171 PPAR ALPHA WIKI- WP2878 DBI; CDK4 1019; 1622 26 26 PATHWAY PATHWAYS 0.00779785 0.03566794 2.108025171 CYCLIN A: CDK2- REACTOME R-HSA- CDKN1A; 1026; 1019 26 26 ASSOCIATED EVENTS 69656 CDK4 AT S PHASE ENTRY 0.00779785 0.03566794 2.108025171 MATURITY ONSET KEGG PATH: NEUROG3; 50674; 3171 26 26 DIABETES OF THE HSA04950 FOXA3 YOUNG - HOMO SAPIENS (HUMAN) 0.0085821 0.03854159 2.066404689 TRIGLYCERIDE REACTOME R-HSA- DBI; 641371; 83 82 BIOSYNTHESIS 75109 TECR; 1622; 9524 ACOT1 0.00900992 0.03904299 2.045279066 INFLUENCE OF BIOCARTA RACCYCD CDKN1A; 1026; 1019 28 28 RAS AND RHO PATHWAY CDK4 PROTEINS ON G1 TO S TRANSITION 0.00900992 0.03904299 2.045279066 NOTCH-NCORE SIGNALINK NONE ADAM10; 4242; 102 28 28 MFNG 0.00936298 0.03987338 2.028585863 METABOLISM OF REACTOME R-HSA- RBP1; 2719; 5947; 164 161 VITAMINS AND 196854 CLPS; 348; 1208 COFACTORS GPC3; APOE 0.0096455 0.04038032 2.015675204 CYCLIN E ASSO- REACTOME R-HSA- CDKN1A; 1026; 1019 29 29 CIATED EVENTS 69202 CDK4 DURINT G1/S TRANSITION
TABLE-US-00005 TABLE 4 SEURAT BIMODAL FOLD LIKELIHOOD CHANGE RATIO WILCOXON MAST CLUSTER ADJUSTED ADJUSTED ADJUSTED GENE NAME X VS. ALL P-VALUE P-VALUE P-VALUE CLUSTER ID SPP1 4.5742486 0 0 4.54E−118 DUCTAL 1 SPARC 3.67597871 0 0 1.47E−131 DUCTAL 1 TMEM45A 3.23772906 2.45E−267 1.84E−301 6.31E−68 DUCTAL 1 1700011H14RIK 2.66511761 9.27E−265 4.18E−250 4.48E−53 DUCTAL 1 ANXA2 2.61031438 2.48E−233 8.87E−231 6.17E−27 DUCTAL 1 S100A11 2.57697996 0 5.41E−269 1.18E−92 DUCTAL 1 MALAT1 2.46702601 0 4.60E−298 1.35E−191 DUCTAL 1 PDZK1IP1 2.36499088 2.36E−209 5.59E−191 3.86E−46 DUCTAL 1 CDKN1A 2.34816666 1.12E−183 4.41E−20 2.62E−54 DUCTAL 1 ENPP2 2.3481392 1.24E−198 2.06E−125 5.30E−85 DUCTAL 1 MEG3 2.27991956 6.34E−234 1.00E−204 7.27E−132 DUCTAL 1 FXYD2 2.27252826 1.45E−144 5.94E−165 2.42E−15 DUCTAL 1 CLU 2.23691983 5.63E−218 2.91E−172 1.35E−82 DUCTAL 1 GAS6 2.23045634 2.39E−172 9.01E−106 2.06E−61 DUCTAL 1 RBP1 2.22717951 1.74E−235 5.69E−182 2.96E−56 DUCTAL 1 S100A10 2.2043717 5.29E−165 1.43E−157 8.76E−26 DUCTAL 1 KRT7 2.19983985 4.34E−194 5.25E−159 4.94E−37 DUCTAL 1 CYM 2.17903903 8.32E−39 1 1.77E−05 DUCTAL 1 CYR61 2.17363728 2.19E−129 1.87E−118 2.76E−16 DUCTAL 1 TINAGL1 2.17096222 6.78E−156 1.63E−159 1.57E−25 DUCTAL 1 GSTA3 2.15182377 1.16E−188 4.66E−152 2.09E−38 DUCTAL 1 ATP1B1 2.1249283 2.83E−222 6.70E−189 1.73E−59 DUCTAL 1 LURAP1L 2.10760262 1.30E−123 4.00E−92 5.74E−15 DUCTAL 1 CYSTM1 2.07965495 1.61E−250 1.82E−189 2.39E−81 DUCTAL 1 CXCL12 2.07309274 4.83E−134 4.06E−145 1.45E−25 DUCTAL 1 KRT18 2.01687548 1.28E−220 7.90E−198 6.13E−53 DUCTAL 1 TM4SF4 2.00919748 6.11E−146 2.41E−88 4.28E−38 DUCTAL 1 CYR61 3.64235201 3.53E−197 1.49E−165 5.41E−105 DUCTAL 2 ATF3 3.11981601 7.74E−132 6.97E−153 2.45E−77 DUCTAL 2 FOS 2.91107534 1.50E−129 4.58E−115 7.74E−83 DUCTAL 2 NR4A1 2.8138114 2.25E−153 5.08E−171 3.49E−92 DUCTAL 2 JUNB 2.40679226 2.86E−108 2.85E−96 1.35E−56 DUCTAL 2 8430408G22RIK 2.37749198 4.85E−34 1.14E−38 7.14E−10 DUCTAL 2 JUN 2.25145709 5.82E−119 9.90E−105 2.70E−55 DUCTAL 2 BTG2 2.24346408 2.39E−79 2.90E−58 6.53E−43 DUCTAL 2 HES1 2.13479968 6.82E−69 3.16E−51 3.06E−26 DUCTAL 2 PPP1R15A 2.13475793 3.19E−97 1.24E−86 1.67E−53 DUCTAL 2 EGR1 2.12490869 2.29E−94 6.80E−92 6.98E−61 DUCTAL 2 DYNLL1 2.11366075 1.45E−154 3.23E−115 4.06E−113 DUCTAL 2 SPP1 2.0999975 7.01E−209 4.55E−85 3.61E−45 DUCTAL 2 KLF6 2.06789809 1.27E−56 1.32E−46 1.95E−29 DUCTAL 2 KRT8 2.05871288 2.60E−151 4.20E−122 2.48E−85 DUCTAL 2 2810417H13RIK 2.74928489 4.32E−174 3.96E−132 1.50E−227 PROLIF. DUCTAL SPC25 2.49675899 4.26E−126 8.09E−119 4.77E−180 PROLIF. DUCTAL CDK1 2.34683179 9.38E−127 9.50E−113 3.30E−200 PROLIF. DUCTAL TOP2A 2.26884946 1.54E−104 2.27E−106 7.24E−191 PROLIF. DUCTAL RRM2 2.267689 2.92E−105 6.25E−95 1.37E−149 PROLIF. DUCTAL NUSAP1 2.18384483 6.65E−74 6.04E−61 2.89E−120 PROLIF. DUCTAL LIG1 2.13873889 2.34E−157 1.86E−108 2.34E−139 PROLIF. DUCTAL TK1 2.08401561 8.64E−139 1.94E−113 6.38E−134 PROLIF. DUCTAL PRC1 2.06306819 1.86E−100 6.48E−94 5.89E−143 PROLIF. DUCTAL GMNN 2.0565317 1.36E−139 1.16E−101 3.68E−131 PROLIF. DUCTAL UBE2C 2.03068945 9.47E−32 1.05E−30 8.08E−78 PROLIF. DUCTAL SPP1 2.02645622 3.97E−192 1.83E−72 3.52E−142 PROLIF. DUCTAL UBE2C 6.03181715 0 5.42E−242 1.64E−227 PROLIF ACINAR CCNB1 3.26754701 1.83E−220 2.37E−186 3.95E−160 PROLIF ACINAR NUSAP1 2.83235587 1.11E−245 0 1.30E−118 PROLIF ACINAR CKS2 2.77358962 1.00E−225 2.38E−157 6.00E−144 PROLIF ACINAR TOP2A 2.71752364 6.13E−189 7.99E−197 1.58E−78 PROLIF ACINAR CDK1 2.68799936 2.08E−194 2.37E−166 9.37E−97 PROLIF ACINAR ARL6IP1 2.65718331 9.56E−188 5.30E−132 2.69E−251 PROLIF ACINAR SPC25 2.63538288 1.11E−185 1.41E−199 9.33E−100 PROLIF ACINAR AURKA 2.53276828 7.74E−210 4.27E−285 8.79E−95 PROLIF ACINAR BIRC5 2.52279362 1.76E−187 2.62E−148 4.28E−116 PROLIF ACINAR H2AFX 2.47566376 2.88E−177 1.53E−138 1.65E−130 PROLIF ACINAR PLK1 2.47141197 1.31E−207 5.53E−269 7.24E−99 PROLIF ACINAR CDC20 2.34020305 2.00E−124 5.61E−129 1.37E−80 PROLIF ACINAR CDCA8 2.33746543 2.01E−156 2.54E−147 4.73E−107 PROLIF ACINAR TUBA1C 2.21424053 9.33E−142 7.67E−138 2.75E−119 PROLIF ACINAR PBK 2.20226659 2.87E−127 6.69E−130 4.55E−43 PROLIF ACINAR CENPF 2.17584059 2.82E−160 3.96E−196 2.16E−96 PROLIF ACINAR HMGB2 2.15815702 3.79E−210 4.89E−131 6.83E−39 PROLIF ACINAR HMMR 2.11885246 1.30E−156 2.81E−190 3.61E−69 PROLIF ACINAR KIF22 2.116951 8.59E−168 1.27E−200 1.53E−93 PROLIF ACINAR BUB3 2.10519912 1.91E−143 9.04E−122 3.18E−142 PROLIF ACINAR PRC1 2.09521226 3.45E−180 6.32E−240 4.76E−72 PROLIF ACINAR SMC4 2.05604272 2.73E−130 1.67E−130 2.78E−75 PROLIF ACINAR AURKB 2.03819235 9.01E−172 3.94E−222 5.57E−50 PROLIF ACINAR CTRB1 5.74993288 0 0 0 ACINAR PNLIPRP1 5.63326234 0 0 0 ACINAR CLPS 5.53917954 0 0 0 ACINAR CPA2 5.37010671 0 0 0 ACINAR SERPINA6 5.08110441 0 0 0 ACINAR CPA1 5.05263211 0 0 0 ACINAR NUPR1 4.89032011 0 0 0 ACINAR REEP5 3.44111343 0 0 0 ACINAR CELA1 3.37399106 0 0 4.96E−249 ACINAR SERPINI2 3.23017738 0 0 9.63E−284 ACINAR SPINK1 3.21102208 0 0 1.09E−160 ACINAR CEL 2.851163 0 0 8.66E−235 ACINAR GSTM1 2.81010809 0 0 6.21E−294 ACINAR PTF1A 2.73621938 0 0 3.17E−219 ACINAR IFITM3 2.70942349 0 0 4.95E−299 ACINAR SEPP1 2.57552155 0 0 8.37E−208 ACINAR GCAT 2.41434554 0 0 7.97E−176 ACINAR TMEM97 2.41052235 0 0 9.94E−186 ACINAR GGH 2.34801558 0 0 1.20E−149 ACINAR GAMT 2.32774802 0 0 4.37E−190 ACINAR FKBP11 2.27632269 0 0 2.15E−158 ACINAR XBP1 2.23150371 0 0 7.40E−164 ACINAR SERPINB1A 2.18661438 0 0 7.18E−165 ACINAR ASNS 2.17171072 0 0 2.53E−125 ACINAR DAP 2.16225051 0 0 4.24E−167 ACINAR SERP1 2.13621614 0 0 3.23E−257 ACINAR VTN 2.11827963 0 0 9.04E−177 ACINAR TMED6 2.10968474 0 3.72E−287 6.22E−125 ACINAR GC 2.03635005 4.71E−306 6.56E−254 2.71E−111 ACINAR MEST 2.00985467 9.94E−194 3.65E−171 6.62E−76 ACINAR
TABLE-US-00006 TABLE 5 MEMBERS.sub.— - LOG MEMBERS.sub.— INPUT.sub.— EFFEC- (P- INPUT.sub.— OVERLAP.sub.— TIVE.sub.— P-VALUE Q-VALUE VALUE) PATHWAY SOURCE OVERLAP GENEIDS SIZE SIZE 4.91E−05 0.01218005 4.30880246 MATURITY ONSET KEGG FOXA2; 15242; 15376; 26 26 DIABETES OF THE GCK; HHEX; 103988; 15285 YOUNG - MUS MNX1 103988; 15285 MUSCULUS (MOUSE) MNX1 0.00016694 0.01541533 3.77743739 BIOGENIC AMINE WIKI- DDC; TPH1; 21990; 14415; 15 14 SYNTHESIS PATHWAYS GAD1 13195 0.00018686 0.01541533 3.7284933 AMPHETAMINE KEGG DDC; ARC; 12313; 14281; 68 67 ADDICTION - MUS FOS; JUN; 16476; 13195; MUSCULUS (MOUSE) CALM1 11838 0.00037295 0.01541533 3.42834762 SEROTONIN AND REACTOME DDC; TPH1 13195; 21990 4 4 MELATONIN BIOSYNTHESIS 0.00037295 0.01541533 3.42834762 SEROTONIN AND MOUSECYC DDC; TPH1 13195; 21990 5 4 MELATONIN BIOSYNTHESIS 0.00037295 0.01541533 3.42834762 BIOSYNTHESIS OF MOUSECYC DDC; TPH1 13195; 21990 5 4 SEROTONIN AND MELATONIN 0.00108385 0.03560609 2.96503052 ESTROGEN SIGNALING KEGG HSP90AB1; 12313; 16440; 99 98 PATHWAY - ITPR3; FOS; 16476; 15516; MUS MUSCULUS CALM1 14281 (MOUSE) 0.00114858 0.03560609 2.93983738 PROTEASOME WIKI- PSMD4; 57296; 19186; 58 58 DEGRADATION PATHWAYS UBE2B; 19185; 22210 PSMD8; PSME1 0.00153742 0.03843282 2.81320804 MAP TARGETS/ REACTOME DUSP6; JUN; 16476; 14281; 29 29 NUCLEAR EVENTS FOS 67603 MEDIATED BY MAP KINASES 0.00160063 0.03843282 2.79570813 MAPK SIGNALING WIKI- FOS; GCK; 16476; 103988; 158 158 PATHWAY PATHWAYS NR4A1; JUN; 67603; 14281; DUSP5; 15370; 240672 DUSP6 0.00170468 0.03843282 2.76835674 FOLDING OF ACTIN REACTOME CCT8; CCT5 12469; 12465 8 8 BY CCT/TRIC 0.00271143 0.05260961 2.56680228 ACTIVATION OF REACTOME JUN; FOS 16476; 14281 10 10 THE AP-1 FAMILY OF TRANSCRIPTION FACTORS 0.00275776 0.05260961 2.55944322 EGFR1 SIGNALING WIKI- FOS; GRB10; 16476; 16668; 176 176 PATHWAY PATHWAYS JUN; SHOC2; 14281; 14786; GRB7; KRT18 14783; 56392 0.00393581 0.06325448 2.40496559 SEROTONIN AND WIKI- ARC; FOS 11838; 14281 12 12 ANXIETY-RELATED PATHWAYS EVENTS 0.00393581 0.06325448 2.40496559 TETRAHYDRO- REACTOME GCH1; 14528; 12313 12 12 BIOPTERIN (BH4) CALM1 SYNTHESIS, RECYCLING, SALVAGE AND REGULATION 0.00462743 0.06325448 2.33466029 FORMATION OF ATP REACTOME ATP5B; 11947; 11946 15 13 BY CHEMIOSMOTIC ATP5A1 COUPLING 0.00511433 0.06325448 2.29121128 PROTEASOME - MUS KEGG PSMD4; 57296; 19185; 44 44 MUSCULUS (MOUSE) PSMD8; 19186 PSME1 0.00537084 0.06325448 2.26995755 AMINE-DERIVED REACTOME DDC; TPH1 21990; 13195 14 14 HORMONES 0.00548654 0.06325448 2.26070114 TRIF-MEDIATED REACTOME DUSP6; JUN; 16476; 14281; 89 89 TLR3/TLR4 SIGNALING UBB; FOS 67603; 22187 0.00548654 0.06325448 2.26070114 MYD88- REACTOME DUSP6; JUN; 16476; 14281; 89 89 INDEPENDENT UBB; FOS 67603; 22187 CASCADE 0.00548654 0.06325448 2.26070114 TOLL LIKE REACTOME DUSP6; JUN; 16476; 14281; 89 89 RECEPTOR 3 (TLR3) UBB; FOS 67603; 22187 CASCADE 0.00561128 0.06325448 2.2509377 SIGNALING BY REACTOME ITPR3; 22187; 15370; 144 143 ERBB2 NR4A1; 14786; 12313; GRB7; UBB; 16440 CALM1 0.00615543 0.06637157 2.21074172 SELENIUM WIKI- SARS; JUN; 16476; 20226; 47 47 METABOLISM- PATHWAYS FOS 14281 SELENOPROTEINS 0.00652849 0.06746104 2.18518738 MAP KINASE REACTOME DUSP6; JUN; 16476; 14281; 48 48 ACTIVATION IN FOS 67603 TLR CASCADE 0.00690134 0.06846131 2.16106648 ACTIVATED TLR4 REACTOME DUSP6; JUN; 16476; 14281; 95 95 SIGNALLING UBB; FOS 67603; 22187 0.00742183 0.07012614 2.12948882 CIRCADIAN KEGG CACNA1H; 12313; 16440; 99 97 ENTRAINMENT - MUS ITPR3; FOS; 58226, 14281 MUSCULUS (MOUSE) CALM1 0.0076347 0.07012614 2.117208 FC EPSILON REACTOME ITPR3; 16476; 14281; 155 154 RECEPTOR (FCERI) NR4A1; JUN; 15370; 12313; SIGNALING CALM1; FOS 16440 0.00884565 0.07834717 2.05327032 SEROTONIN AND WIKI- ARC; FOS 11838; 14281 19 18 ANXIETY PATHWAYS 0.00983551 0.08036762 2.00720299 ENOS ACTIVATION REACTOME GCH1; 14528; 12313 19 19 AND REGULATION CALM1 0.00983551 0.08036762 2.00720299 METABOLISM OF REACTOME GCH1; 14528; 12313 19 19 NITRICOXIDE CALM1